Recent from talks
Nothing was collected or created yet.
Transistor count
View on Wikipedia
| Semiconductor device fabrication |
|---|
|
MOSFET scaling (process nodes) |
The transistor count is the number of transistors in an electronic device (typically on a single substrate or silicon die). It is the most common measure of integrated circuit complexity (although the majority of transistors in modern microprocessors are contained in cache memories, which consist mostly of the same memory cell circuits replicated many times). The rate at which MOS transistor counts have increased generally follows Moore's law, which observes that transistor count doubles approximately every two years. However, being directly proportional to the area of a die, transistor count does not represent how advanced the corresponding manufacturing technology is. A better indication of this is transistor density which is the ratio of a semiconductor's transistor count to its die area.
Records
[edit]As of 2023[update], the highest transistor count in flash memory is Micron's 2 terabyte (3D-stacked) 16-die, 232-layer V-NAND flash memory chip, with 5.3 trillion floating-gate MOSFETs (3 bits per transistor).
The highest transistor count in a single chip processor as of 2020[update] is that of the deep learning processor Wafer Scale Engine 2 by Cerebras. It has 2.6 trillion MOSFETs in 84 exposed fields (dies) on a wafer, manufactured using TSMC's 7 nm FinFET process.[1][2][3][4][5]
As of 2024[update], the GPU with the highest transistor count is Nvidia's Blackwell-based B100 accelerator, built on TSMC's custom 4NP process node and totaling 208 billion MOSFETs.
The highest transistor count in a consumer microprocessor as of March 2025[update] is 184 billion transistors, in Apple's ARM-based dual-die M3 Ultra SoC, which is fabricated using TSMC's 3 nm semiconductor manufacturing process.[citation needed]
| Year | Component | Name | Number of MOSFETs (in trillions) |
Remarks |
|---|---|---|---|---|
| 2022 | Flash memory | Micron's V-NAND module | 5.3 | stacked package of sixteen 232-layer 3D NAND dies |
| 2020 | any processor | Wafer Scale Engine 2 | 2.6 | wafer-scale design of 84 exposed fields (dies) |
| 2024 | GPU | Nvidia B100 | 0.208 | Uses two reticle limit dies, with 104 billion transistors each, joined together and acting as a single large monolithic piece of silicon |
| 2025 | Microprocessor (consumer) |
Apple M3 Ultra | 0.184 | SoC using two dies joined together with a high-speed bridge |
| 2020 | DLP | Colossus Mk2 GC200 | 0.059 | An IPU[a] (Intelligence Processing Unit) in contrast to CPU and GPU |
In terms of computer systems that consist of numerous integrated circuits, the supercomputer with the highest transistor count as of 2016[update] was the Chinese-designed Sunway TaihuLight, which has for all CPUs/nodes combined "about 400 trillion transistors in the processing part of the hardware" and "the DRAM includes about 12 quadrillion transistors, and that's about 97 percent of all the transistors."[6] To compare, the smallest computer, as of 2018[update] dwarfed by a grain of rice, had on the order of 100,000 transistors. Early experimental solid-state computers had as few as 130 transistors but used large amounts of diode logic. The first carbon nanotube computer had 178 transistors and was a 1-bit one-instruction set computer, while a later one is 16-bit (its instruction set is 32-bit RISC-V though).
Ionic transistor chips ("water-based" analog limited processor), have up to hundreds of such transistors.[7]
Estimates of the total numbers of transistors manufactured:
Transistor count
[edit]
Microprocessors
[edit]This subsection needs additional citations for verification. (December 2019) |
A microprocessor incorporates the functions of a computer's central processing unit on a single integrated circuit. It is a multi-purpose, programmable device that accepts digital data as input, processes it according to instructions stored in its memory, and provides results as output.
The development of MOS integrated circuit technology in the 1960s led to the development of the first microprocessors.[10] The 20-bit MP944, developed by Garrett AiResearch for the U.S. Navy's F-14 Tomcat fighter in 1970, is considered by its designer Ray Holt to be the first microprocessor.[11] It was a multi-chip microprocessor, fabricated on six MOS chips. However, it was classified by the Navy until 1998. The 4-bit Intel 4004, released in 1971, was the first single-chip microprocessor.
Modern microprocessors typically include on-chip cache memories. The number of transistors used for these cache memories typically far exceeds the number of transistors used to implement the logic of the microprocessor (that is, excluding the cache). For example, the last DEC Alpha chip uses 90% of its transistors for cache.[12]
| Processor | Transistor count | Year | Designer | Process (nm) |
Area (mm2) | Transistor density (tr./mm2) |
|---|---|---|---|---|---|---|
| MP944 (20-bit, 6-chip, 28 chips total) | 74,442 (5,360 excl. ROM & RAM)[13][14] | 1970[11][b] | Garrett AiResearch | ? | ? | ? |
| Intel 4004 (4-bit, 16-pin) | 2,250 | 1971 | Intel | 10,000 nm | 12 mm2 | 188 |
| TMX 1795 (8-bit, 24-pin) | 3,078[15] | 1971 | Texas Instruments | ? | 30.64 mm2 | 100.5 |
| Intel 8008 (8-bit, 18-pin) | 3,500 | 1972 | Intel | 10,000 nm | 14 mm2 | 250 |
| NEC μCOM-4 (4-bit, 42-pin) | 2,500[16][17] | 1973 | NEC | 7,500 nm[18] | ? | ? |
| Toshiba TLCS-12 (12-bit) | 11,000+[19] | 1973 | Toshiba | 6,000 nm | 32.45 mm2 | 340+ |
| Intel 4040 (4-bit, 16-pin) | 3,000 | 1974 | Intel | 10,000 nm | 12 mm2 | 250 |
| Motorola 6800 (8-bit, 40-pin) | 4,100 | 1974 | Motorola | 6,000 nm | 16 mm2 | 256 |
| Intel 8080 (8-bit, 40-pin) | 6,000 | 1974 | Intel | 6,000 nm | 20 mm2 | 300 |
| TMS 1000 (4-bit, 28-pin) | 8,000[c] | 1974[20] | Texas Instruments | 8,000 nm | 11 mm2 | 730 |
| HP Nanoprocessor (8-bit, 40-pin) | 4639[d][21] | 1974 | Hewlett-Packard | ? | 19 mm2 | ? |
| MOS Technology 6502 (8-bit, 40-pin) | 4,528[e][22] | 1975 | MOS Technology | 8,000 nm | 21 mm2 | 216 |
| Intersil IM6100 (12-bit, 40-pin; clone of PDP-8) | 4,000 | 1975 | Intersil | ? | ? | ? |
| CDP 1801 (8-bit, 2-chip, 40-pin) | 5,000 | 1975 | RCA | ? | ? | ? |
| RCA 1802 (8-bit, 40-pin) | 5,000 | 1976 | RCA | 5,000 nm | 27 mm2 | 185 |
| Zilog Z80 (8-bit, 4-bit ALU, 40-pin) | 8,500[f] | 1976 | Zilog | 4,000 nm | 18 mm2 | 470 |
| Intel 8085 (8-bit, 40-pin) | 6,500 | 1976 | Intel | 3,000 nm | 20 mm2 | 325 |
| TMS9900 (16-bit) | 8,000 | 1976 | Texas Instruments | ? | ? | ? |
| Bellmac-8 (8-bit) | 7,000 | 1977 | Bell Labs | 5,000 nm | ? | ? |
| Motorola 6809 (8-bit with some 16-bit features, 40-pin) | 9,000 | 1978 | Motorola | 5,000 nm | 21 mm2 | 430 |
| Intel 8086 (16-bit, 40-pin) | 29,000[23] | 1978 | Intel | 3,000 nm | 33 mm2 | 880 |
| Zilog Z8000 (16-bit) | 17,500[24] | 1979 | Zilog | 5,000-6,000 nm (design rules) | 39.31 mm2 (238x256 mil2) | 445 |
| Intel 8088 (16-bit, 8-bit data bus) | 29,000 | 1979 | Intel | 3,000 nm | 33 mm2 | 880 |
| Motorola 68000 (16/32-bit, 32-bit registers, 16-bit ALU) | 68,000[25] | 1979 | Motorola | 3,500 nm | 44 mm2 | 1,550 |
| Intel 8051 (8-bit, 40-pin) | 50,000 | 1980 | Intel | ? | ? | ? |
| WDC 65C02 | 11,500[26] | 1981 | WDC | 3,000 nm | 6 mm2 | 1,920 |
| ROMP (32-bit) | 45,000 | 1981 | IBM | 2,000 nm | 58.52 mm2 | 770 |
| Intel 80186 (16-bit, 68-pin) | 55,000 | 1982 | Intel | 3,000 nm | 60 mm2 | 920 |
| Intel 80286 (16-bit, 68-pin) | 134,000 | 1982 | Intel | 1,500 nm | 49 mm2 | 2,730 |
| WDC 65C816 (8/16-bit) | 22,000[27] | 1983 | WDC | 3,000 nm[28] | 9 mm2 | 2,400 |
| NEC V20 | 63,000 | 1984 | NEC | ? | ? | ? |
| Motorola 68020 (32-bit; 114 pins used) | 190,000[29] | 1984 | Motorola | 2,000 nm | 85 mm2 | 2,200 |
| Intel 80386 (32-bit, 132-pin; no cache) | 275,000 | 1985 | Intel | 1,500 nm | 104 mm2 | 2,640 |
| ARM 1 (32-bit; no cache) | 25,000[29] | 1985 | Acorn | 3,000 nm | 50 mm2 | 500 |
| Novix NC4016 (16-bit) | 16,000[30] | 1985[31] | Harris Corporation | 3,000 nm[32] | ? | ? |
| SPARC MB86900 (32-bit; no cache) | 110,000[33] | 1986 | Fujitsu | 1,200 nm | ? | ? |
| NEC V60[34] (32-bit; no cache) | 375,000 | 1986 | NEC | 1,500 nm | ? | ? |
| ARM 2 (32-bit, 84-pin; no cache) | 27,000[35][29] | 1986 | Acorn | 2,000 nm | 30.25 mm2 | 890 |
| Z80000 (32-bit; very small cache) | 91,000 | 1986 | Zilog | ? | ? | ? |
| NEC V70[34] (32-bit; no cache) | 385,000 | 1987 | NEC | 1,500 nm | ? | ? |
| Hitachi Gmicro/200[36] | 730,000 | 1987 | Hitachi | 1,000 nm | ? | ? |
| Motorola 68030 (32-bit, very small caches) | 273,000 | 1987 | Motorola | 800 nm | 102 mm2 | 2,680 |
| TI Explorer's 32-bit Lisp machine chip | 553,000[37] | 1987 | Texas Instruments | 2,000 nm[38] | ? | ? |
| DEC WRL MultiTitan | 180,000[39] | 1988 | DEC WRL | 1,500 nm | 61 mm2 | 2,950 |
| Intel i960 (32-bit, 33-bit memory subsystem, no cache) | 250,000[40] | 1988 | Intel | 1,500 nm[41] | ? | ? |
| Intel i960CA (32-bit, cache) | 600,000[41] | 1989 | Intel | 800 nm | 143 mm2 | 4,200 |
| Intel i860 (32/64-bit, 128-bit SIMD, cache, VLIW) | 1,000,000[42] | 1989 | Intel | ? | ? | ? |
| Intel 80486 (32-bit, 8 KB cache) | 1,180,235 | 1989 | Intel | 1,000 nm | 173 mm2 | 6,822 |
| ARM 3 (32-bit, 4 KB cache) | 310,000 | 1989 | Acorn | 1,500 nm | 87 mm2 | 3,600 |
| POWER1 (9-chip module, 72 kB of cache) | 6,900,000[43] | 1990 | IBM | 1,000 nm | 1,283.61 mm2 | 5,375 |
| Motorola 68040 (32-bit, 8 KB caches) | 1,200,000 | 1990 | Motorola | 650 nm | 152 mm2 | 7,900 |
| R4000 (64-bit, 16 KB of caches) | 1,350,000 | 1991 | MIPS | 1,000 nm | 213 mm2 | 6,340 |
| ARM 6 (32-bit, no cache for this 60 variant) | 35,000 | 1991 | ARM | 800 nm | ? | ? |
| Hitachi SH-1 (32-bit, no cache) | 600,000[44] | 1992[45] | Hitachi | 800 nm | 100 mm2 | 6,000 |
| Intel i960CF (32-bit, cache) | 900,000[41] | 1992 | Intel | ? | 125 mm2 | 7,200 |
| Alpha 21064 (64-bit, 290-pin; 16 KB of caches) | 1,680,000 | 1992 | DEC | 750 nm | 233.52 mm2 | 7,190 |
| Hitachi HARP-1 (32-bit, cache) | 2,800,000[46] | 1993 | Hitachi | 500 nm | 267 mm2 | 10,500 |
| Pentium (32-bit, 16 KB of caches) | 3,100,000 | 1993 | Intel | 800 nm | 294 mm2 | 10,500 |
| POWER2 (8-chip module, 288 kB of cache) | 23,037,000[47] | 1993 | IBM | 720 nm | 1,217.39 mm2 | 18,923 |
| ARM700 (32-bit; 8 KB cache) | 578,977[48] | 1994 | ARM | 700 nm | 68.51 mm2 | 8,451 |
| MuP21 (21-bit,[49] 40-pin; includes video) | 7,000[50] | 1994 | Offete Enterprises | 1,200 nm | ? | ? |
| Motorola 68060 (32-bit, 16 KB of caches) | 2,500,000 | 1994 | Motorola | 600 nm | 218 mm2 | 11,500 |
| PowerPC 601 (32-bit, 32 KB of caches) | 2,800,000[51] | 1994 | Apple, IBM, Motorola | 600 nm | 121 mm2 | 23,000 |
| PowerPC 603 (32-bit, 16 KB of caches) | 1,600,000[52] | 1994 | Apple, IBM, Motorola | 500 nm | 84.76 mm2 | 18,900 |
| PowerPC 603e (32-bit, 32 KB of caches) | 2,600,000[53] | 1995 | Apple, IBM, Motorola | 500 nm | 98 mm2 | 26,500 |
| Alpha 21164 EV5 (64-bit, 112 kB cache) | 9,300,000[54] | 1995 | DEC | 500 nm | 298.65 mm2 | 31,140 |
| SA-110 (32-bit, 32 KB of caches) | 2,500,000[29] | 1995 | Acorn, DEC, Apple | 350 nm | 50 mm2 | 50,000 |
| Pentium Pro (32-bit, 16 KB of caches;[55] L2 cache on-package, but on separate die) | 5,500,000[56] | 1995 | Intel | 500 nm | 307 mm2 | 18,000 |
| PA-8000 64-bit, no cache | 3,800,000[57] | 1995 | HP | 500 nm | 337.69 mm2 | 11,300 |
| Alpha 21164A EV56 (64-bit, 112 kB cache) | 9,660,000[58] | 1996 | DEC | 350 nm | 208.8 mm2 | 46,260 |
| AMD K5 (32-bit, caches) | 4,300,000 | 1996 | AMD | 500 nm | 251 mm2 | 17,000 |
| Pentium II Klamath (32-bit, 64-bit SIMD, caches) | 7,500,000 | 1997 | Intel | 350 nm | 195 mm2 | 39,000 |
| AMD K6 (32-bit, caches) | 8,800,000 | 1997 | AMD | 350 nm | 162 mm2 | 54,000 |
| F21 (21-bit; includes e.g. video) | 15,000 | 1997[50] | Offete Enterprises | ? | ? | ? |
| AVR (8-bit, 40-pin; w/memory) | 140,000 (48,000 excl. memory[59]) |
1997 | Nordic VLSI/Atmel | ? | ? | ? |
| Pentium II Deschutes (32-bit, large cache) | 7,500,000 | 1998 | Intel | 250 nm | 113 mm2 | 66,000 |
| Alpha 21264 EV6 (64-bit) | 15,200,000[60] | 1998 | DEC | 350 nm | 313.96 mm2 | 48,400 |
| Alpha 21164PC PCA57 (64-bit, 48 kB cache) | 5,700,000 | 1998 | Samsung | 280 nm | 100.5 mm2 | 56,700 |
| Hitachi SH-4 (32-bit, caches)[61] | 3,200,000[62] | 1998 | Hitachi | 250 nm | 57.76 mm2 | 55,400 |
| ARM 9TDMI (32-bit, no cache) | 111,000[29] | 1999 | Acorn | 350 nm | 4.8 mm2 | 23,100 |
| Pentium III Katmai (32-bit, 128-bit SIMD, caches) | 9,500,000 | 1999 | Intel | 250 nm | 128 mm2 | 74,000 |
| Emotion Engine (64-bit, 128-bit SIMD, cache) | 10,500,000[63] – 13,500,000[64] |
1999 | Sony, Toshiba | 250 nm | 239.7 mm2[63] | 43,800 – 56,300 |
| Pentium II Mobile Dixon (32-bit, caches) | 27,400,000 | 1999 | Intel | 180 nm | 180 mm2 | 152,000 |
| AMD K6-III (32-bit, caches) | 21,300,000 | 1999 | AMD | 250 nm | 118 mm2 | 181,000 |
| AMD K7 (32-bit, caches) | 22,000,000 | 1999 | AMD | 250 nm | 184 mm2 | 120,000 |
| Gekko (32-bit, large cache) | 21,000,000[65] | 2000 | IBM, Nintendo | 180 nm | 43 mm2 | 490,000 (check) |
| Pentium III Coppermine (32-bit, large cache) | 21,000,000 | 2000 | Intel | 180 nm | 80 mm2 | 263,000 |
| Pentium 4 Willamette (32-bit, large cache) | 42,000,000 | 2000 | Intel | 180 nm | 217 mm2 | 194,000 |
| SPARC64 V (64-bit, large cache) | 191,000,000[66] | 2001 | Fujitsu | 130 nm[67] | 290 mm2 | 659,000 |
| Pentium III Tualatin (32-bit, large cache) | 45,000,000 | 2001 | Intel | 130 nm | 81 mm2 | 556,000 |
| Pentium 4 Northwood (32-bit, large cache) | 55,000,000 | 2002 | Intel | 130 nm | 145 mm2 | 379,000 |
| Itanium 2 McKinley (64-bit, large cache) | 220,000,000 | 2002 | Intel | 180 nm | 421 mm2 | 523,000 |
| Alpha 21364 (64-bit, 946-pin, SIMD, very large caches) | 152,000,000[12] | 2003 | DEC | 180 nm | 397 mm2 | 383,000 |
| AMD K7 Barton (32-bit, large cache) | 54,300,000 | 2003 | AMD | 130 nm | 101 mm2 | 538,000 |
| AMD K8 (64-bit, large cache) | 105,900,000 | 2003 | AMD | 130 nm | 193 mm2 | 548,700 |
| Pentium M Banias (32-bit) | 77,000,000[68] | 2003 | Intel | 130 nm | 83 mm2 | 928,000 |
| Itanium 2 Madison 6M (64-bit) | 410,000,000 | 2003 | Intel | 130 nm | 374 mm2 | 1,096,000 |
| PlayStation 2 single chip (CPU + GPU) | 53,500,000[69] | 2003[70] | Sony, Toshiba | 90 nm[71] 130 nm[72][73] |
86 mm2 | 622,100 |
| Pentium 4 Prescott (32-bit, large cache) | 112,000,000 | 2004 | Intel | 90 nm | 110 mm2 | 1,018,000 |
| Pentium M Dothan (32-bit) | 144,000,000[74] | 2004 | Intel | 90 nm | 87 mm2 | 1,655,000 |
| SPARC64 V+ (64-bit, large cache) | 400,000,000[75] | 2004 | Fujitsu | 90 nm | 294 mm2 | 1,360,000 |
| Itanium 2 (64-bit;9 MB cache) | 592,000,000 | 2004 | Intel | 130 nm | 432 mm2 | 1,370,000 |
| Pentium 4 Prescott-2M (32-bit, large cache) | 169,000,000 | 2005 | Intel | 90 nm | 143 mm2 | 1,182,000 |
| Pentium D Smithfield (64-bit, large cache) | 228,000,000 | 2005 | Intel | 90 nm | 206 mm2 | 1,107,000 |
| Xenon (64-bit, 128-bit SIMD, large cache) | 165,000,000 | 2005 | IBM | 90 nm | ? | ? |
| Cell (32-bit, cache) | 250,000,000[76] | 2005 | Sony, IBM, Toshiba | 90 nm | 221 mm2 | 1,131,000 |
| Pentium 4 Cedar Mill (32-bit, large cache) | 184,000,000 | 2006 | Intel | 65 nm | 90 mm2 | 2,044,000 |
| Pentium D Presler (64-bit, large cache) | 362,000,000 [77] | 2006 | Intel | 65 nm | 162 mm2 | 2,235,000 |
| Core 2 Duo Conroe (dual-core 64-bit, large caches) | 291,000,000 | 2006 | Intel | 65 nm | 143 mm2 | 2,035,000 |
| Dual-core Itanium 2 (64-bit, SIMD, large caches) | 1,700,000,000[78] | 2006 | Intel | 90 nm | 596 mm2 | 2,852,000 |
| AMD K10 quad-core 2M L3 (64-bit, large caches) | 463,000,000[79] | 2007 | AMD | 65 nm | 283 mm2 | 1,636,000 |
| ARM Cortex-A9 (32-bit, (optional) SIMD, caches) | 26,000,000[80] | 2007 | ARM | 45 nm | 31 mm2 | 839,000 |
| Core 2 Duo Wolfdale (dual-core 64-bit, SIMD, caches) | 411,000,000 | 2007 | Intel | 45 nm | 107 mm2 | 3,841,000 |
| POWER6 (64-bit, large caches) | 789,000,000 | 2007 | IBM | 65 nm | 341 mm2 | 2,314,000 |
| Core 2 Duo Allendale (dual-core 64-bit, SIMD, large caches) | 169,000,000 | 2007 | Intel | 65 nm | 111 mm2 | 1,523,000 |
| Uniphier | 250,000,000[81] | 2007 | Matsushita | 45 nm | ? | ? |
| SPARC64 VI (64-bit, SIMD, large caches) | 540,000,000 | 2007[82] | Fujitsu | 90 nm | 421 mm2 | 1,283,000 |
| Core 2 Duo Wolfdale 3M (dual-core 64-bit, SIMD, large caches) | 230,000,000 | 2008 | Intel | 45 nm | 83 mm2 | 2,771,000 |
| Core i7 (quad-core 64-bit, SIMD, large caches) | 731,000,000 | 2008 | Intel | 45 nm | 263 mm2 | 2,779,000 |
| AMD K10 quad-core 6M L3 (64-bit, SIMD, large caches) | 758,000,000[79] | 2008 | AMD | 45 nm | 258 mm2 | 2,938,000 |
| Atom (32-bit, large cache) | 47,000,000 | 2008 | Intel | 45 nm | 24 mm2 | 1,958,000 |
| SPARC64 VII (64-bit, SIMD, large caches) | 600,000,000 | 2008[83] | Fujitsu | 65 nm | 445 mm2 | 1,348,000 |
| Six-core Xeon 7400 (64-bit, SIMD, large caches) | 1,900,000,000 | 2008 | Intel | 45 nm | 503 mm2 | 3,777,000 |
| Six-core Opteron 2400 (64-bit, SIMD, large caches) | 904,000,000 | 2009 | AMD | 45 nm | 346 mm2 | 2,613,000 |
| SPARC64 VIIIfx (64-bit, SIMD, large caches) | 760,000,000[84] | 2009 | Fujitsu | 45 nm | 513 mm2 | 1,481,000 |
| Atom (Pineview) 64-bit, 1-core, 512 kB L2 cache | 123,000,000[85] | 2010 | Intel | 45 nm | 66 mm2 | 1,864,000 |
| Atom (Pineview) 64-bit, 2-core, 1 MB L2 cache | 176,000,000[86] | 2010 | Intel | 45 nm | 87 mm2 | 2,023,000 |
| SPARC T3 (16-core 64-bit, SIMD, large caches) | 1,000,000,000[87] | 2010 | Sun/Oracle | 40 nm | 377 mm2 | 2,653,000 |
| Six-core Core i7 (Gulftown) | 1,170,000,000 | 2010 | Intel | 32 nm | 240 mm2 | 4,875,000 |
| POWER7 32M L3 (8-core 64-bit, SIMD, large caches) | 1,200,000,000 | 2010 | IBM | 45 nm | 567 mm2 | 2,116,000 |
| Quad-core z196[88] (64-bit, very large caches) | 1,400,000,000 | 2010 | IBM | 45 nm | 512 mm2 | 2,734,000 |
| Quad-core Itanium Tukwila (64-bit, SIMD, large caches) | 2,000,000,000[89] | 2010 | Intel | 65 nm | 699 mm2 | 2,861,000 |
| Xeon Nehalem-EX (8-core 64-bit, SIMD, large caches) | 2,300,000,000[90] | 2010 | Intel | 45 nm | 684 mm2 | 3,363,000 |
| SPARC64 IXfx (64-bit, SIMD, large caches) | 1,870,000,000[91] | 2011 | Fujitsu | 40 nm | 484 mm2 | 3,864,000 |
| Quad-core + GPU Core i7 (64-bit, SIMD, large caches) | 1,160,000,000 | 2011 | Intel | 32 nm | 216 mm2 | 5,370,000 |
| Six-core Core i7/8-core Xeon E5 (Sandy Bridge-E/EP) (64-bit, SIMD, large caches) |
2,270,000,000[92] | 2011 | Intel | 32 nm | 434 mm2 | 5,230,000 |
| Xeon Westmere-EX (10-core 64-bit, SIMD, large caches) | 2,600,000,000 | 2011 | Intel | 32 nm | 512 mm2 | 5,078,000 |
| Atom "Medfield" (64-bit) | 432,000,000[93] | 2012 | Intel | 32 nm | 64 mm2 | 6,750,000 |
| SPARC64 X (64-bit, SIMD, caches) | 2,990,000,000[94] | 2012 | Fujitsu | 28 nm | 600 mm2 | 4,983,000 |
| AMD Bulldozer (8-core 64-bit, SIMD, caches) | 1,200,000,000[95] | 2012 | AMD | 32 nm | 315 mm2 | 3,810,000 |
| Quad-core + GPU AMD Trinity (64-bit, SIMD, caches) | 1,303,000,000 | 2012 | AMD | 32 nm | 246 mm2 | 5,297,000 |
| Quad-core + GPU Core i7 Ivy Bridge (64-bit, SIMD, caches) | 1,400,000,000 | 2012 | Intel | 22 nm | 160 mm2 | 8,750,000 |
| POWER7+ (8-core 64-bit, SIMD, 80 MB L3 cache) | 2,100,000,000 | 2012 | IBM | 32 nm | 567 mm2 | 3,704,000 |
| Six-core zEC12 (64-bit, SIMD, large caches) | 2,750,000,000 | 2012 | IBM | 32 nm | 597 mm2 | 4,606,000 |
| Itanium Poulson (8-core 64-bit, SIMD, caches) | 3,100,000,000 | 2012 | Intel | 32 nm | 544 mm2 | 5,699,000 |
| Xeon Phi (61-core 32-bit, 512-bit SIMD, caches) | 5,000,000,000[96] | 2012 | Intel | 22 nm | 720 mm2 | 6,944,000 |
| Apple A7 (dual-core 64/32-bit ARM64, "mobile SoC", SIMD, caches) | 1,000,000,000 | 2013 | Apple | 28 nm | 102 mm2 | 9,804,000 |
| Six-core Core i7 Ivy Bridge E (64-bit, SIMD, caches) | 1,860,000,000 | 2013 | Intel | 22 nm | 256 mm2 | 7,266,000 |
| POWER8 (12-core 64-bit, SIMD, caches) | 4,200,000,000 | 2013 | IBM | 22 nm | 650 mm2 | 6,462,000 |
| Xbox One main SoC (64-bit, SIMD, caches) | 5,000,000,000 | 2013 | Microsoft, AMD | 28 nm | 363 mm2 | 13,770,000 |
| Quad-core + GPU Core i7 Haswell (64-bit, SIMD, caches) | 1,400,000,000[97] | 2014 | Intel | 22 nm | 177 mm2 | 7,910,000 |
| Apple A8 (dual-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 2,000,000,000 | 2014 | Apple | 20 nm | 89 mm2 | 22,470,000 |
| Core i7 Haswell-E (8-core 64-bit, SIMD, caches) | 2,600,000,000[98] | 2014 | Intel | 22 nm | 355 mm2 | 7,324,000 |
| Apple A8X (tri-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 3,000,000,000[99] | 2014 | Apple | 20 nm | 128 mm2 | 23,440,000 |
| Xeon Ivy Bridge-EX (15-core 64-bit, SIMD, caches) | 4,310,000,000[100] | 2014 | Intel | 22 nm | 541 mm2 | 7,967,000 |
| Xeon Haswell-E5 (18-core 64-bit, SIMD, caches) | 5,560,000,000[101] | 2014 | Intel | 22 nm | 661 mm2 | 8,411,000 |
| Quad-core + GPU GT2 Core i7 Skylake K (64-bit, SIMD, caches) | 1,750,000,000 | 2015 | Intel | 14 nm | 122 mm2 | 14,340,000 |
| Dual-core + GPU Iris Core i7 Broadwell-U (64-bit, SIMD, caches) | 1,900,000,000[102] | 2015 | Intel | 14 nm | 133 mm2 | 14,290,000 |
| Apple A9 (dual-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 2,000,000,000+ | 2015 | Apple | 14 nm (Samsung) |
96 mm2 (Samsung) |
20,800,000+ |
| 16 nm (TSMC) |
104.5 mm2 (TSMC) |
19,100,000+ | ||||
| Apple A9X (dual core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 3,000,000,000+ | 2015 | Apple | 16 nm | 143.9 mm2 | 20,800,000+ |
| IBM z13 (64-bit, caches) | 3,990,000,000 | 2015 | IBM | 22 nm | 678 mm2 | 5,885,000 |
| IBM z13 Storage Controller | 7,100,000,000 | 2015 | IBM | 22 nm | 678 mm2 | 10,472,000 |
| SPARC M7 (32-core 64-bit, SIMD, caches) | 10,000,000,000[103] | 2015 | Oracle | 20 nm | ? | ? |
| Core i7 Broadwell-E (10-core 64-bit, SIMD, caches) | 3,200,000,000[104] | 2016 | Intel | 14 nm | 246 mm2[105] | 13,010,000 |
| Apple A10 Fusion (quad-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 3,300,000,000 | 2016 | Apple | 16 nm | 125 mm2 | 26,400,000 |
| HiSilicon Kirin 960 (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 4,000,000,000[106] | 2016 | Huawei | 16 nm | 110.00 mm2 | 36,360,000 |
| Xeon Broadwell-E5 (22-core 64-bit, SIMD, caches) | 7,200,000,000[107] | 2016 | Intel | 14 nm | 456 mm2 | 15,790,000 |
| Xeon Phi (72-core 64-bit, 512-bit SIMD, caches) | 8,000,000,000 | 2016 | Intel | 14 nm | 683 mm2 | 11,710,000 |
| Zip CPU (32-bit, for FPGAs) | 1,286 6-LUTs[108] | 2016 | Gisselquist Technology | ? | ? | ? |
| Qualcomm Snapdragon 835 (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 3,000,000,000[109][110] | 2016 | Qualcomm | 10 nm | 72.3 mm2 | 41,490,000 |
| Apple A11 Bionic (hexa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 4,300,000,000 | 2017 | Apple | 10 nm | 89.23 mm2 | 48,190,000 |
| AMD Zen CCX (core complex unit: 4 cores, 8 MB L3 cache) | 1,400,000,000[111] | 2017 | AMD | 14 nm (GF 14LPP) |
44 mm2 | 31,800,000 |
| AMD Zeppelin SoC Ryzen (64-bit, SIMD, caches) | 4,800,000,000[112] | 2017 | AMD | 14 nm | 192 mm2 | 25,000,000 |
| AMD Ryzen 5 1600 Ryzen (64-bit, SIMD, caches) | 4,800,000,000[113] | 2017 | AMD | 14 nm | 213 mm2 | 22,530,000 |
| IBM z14 (64-bit, SIMD, caches) | 6,100,000,000 | 2017 | IBM | 14 nm | 696 mm2 | 8,764,000 |
| IBM z14 Storage Controller (64-bit) | 9,700,000,000 | 2017 | IBM | 14 nm | 696 mm2 | 13,940,000 |
| HiSilicon Kirin 970 (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 5,500,000,000[114] | 2017 | Huawei | 10 nm | 96.72 mm2 | 56,900,000 |
| Xbox One X (Project Scorpio) main SoC (64-bit, SIMD, caches) | 7,000,000,000[115] | 2017 | Microsoft, AMD | 16 nm | 360 mm2[115] | 19,440,000 |
| Xeon Platinum 8180 (28-core 64-bit, SIMD, caches) | 8,000,000,000[116] | 2017 | Intel | 14 nm | ? | ? |
| Xeon (unspecified) | 7,100,000,000[117] | 2017 | Intel | 14 nm | 672 mm2 | 10,570,000 |
| POWER9 (64-bit, SIMD, caches) | 8,000,000,000 | 2017 | IBM | 14 nm | 695 mm2 | 11,500,000 |
| Freedom U500 Base Platform Chip (E51, 4×U54) RISC-V (64-bit, caches) | 250,000,000[118] | 2017 | SiFive | 28 nm | ~30 mm2 | 8,330,000 |
| SPARC64 XII (12-core 64-bit, SIMD, caches) | 5,450,000,000[119] | 2017 | Fujitsu | 20 nm | 795 mm2 | 6,850,000 |
| Apple A10X Fusion (hexa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 4,300,000,000[120] | 2017 | Apple | 10 nm | 96.40 mm2 | 44,600,000 |
| Centriq 2400 (64/32-bit, SIMD, caches) | 18,000,000,000[121] | 2017 | Qualcomm | 10 nm | 398 mm2 | 45,200,000 |
| AMD Epyc (32-core 64-bit, SIMD, caches) | 19,200,000,000 | 2017 | AMD | 14 nm | 768 mm2 | 25,000,000 |
| Qualcomm Snapdragon 845 (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 5,300,000,000[122] | 2017 | Qualcomm | 10 nm | 94 mm2 | 56,400,000 |
| Qualcomm Snapdragon 850 (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 5,300,000,000[123] | 2017 | Qualcomm | 10 nm | 94 mm2 | 56,400,000 |
| HiSilicon Kirin 710 (octa-core ARM64 "mobile SoC", SIMD, caches) | 5,500,000,000[124] | 2018 | Huawei | 12 nm | ? | ? |
| Apple A12 Bionic (hexa-core ARM64 "mobile SoC", SIMD, caches) | 6,900,000,000 [125][126] |
2018 | Apple | 7 nm | 83.27 mm2 | 82,900,000 |
| HiSilicon Kirin 980 (octa-core ARM64 "mobile SoC", SIMD, caches) | 6,900,000,000[127] | 2018 | Huawei | 7 nm | 74.13 mm2 | 93,100,000 |
| Qualcomm Snapdragon 8cx / SCX8180 (octa-core ARM64 "mobile SoC", SIMD, caches) | 8,500,000,000[128] | 2018 | Qualcomm | 7 nm | 112 mm2 | 75,900,000 |
| Apple A12X Bionic (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 10,000,000,000[129] | 2018 | Apple | 7 nm | 122 mm2 | 82,000,000 |
| Fujitsu A64FX (64/32-bit, SIMD, caches) | 8,786,000,000[130] | 2018[131] | Fujitsu | 7 nm | ? | ? |
| Tegra Xavier SoC (64/32-bit) | 9,000,000,000[132] | 2018 | Nvidia | 12 nm | 350 mm2 | 25,700,000 |
| Qualcomm Snapdragon 855 (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 6,700,000,000[133] | 2018 | Qualcomm | 7 nm | 73 mm2 | 91,800,000 |
| AMD Zen 2 core (0.5 MB L2 + 4 MB L3 cache) | 475,000,000[134] | 2019 | AMD | 7 nm | 7.83 mm2 | 60,664,000 |
| AMD Zen 2 CCX (core complex: 4 cores, 16 MB L3 cache) | 1,900,000,000[134] | 2019 | AMD | 7 nm | 31.32 mm2 | 60,664,000 |
| AMD Zen 2 CCD (core complex die: 8 cores, 32 MB L3 cache) | 3,800,000,000[134] | 2019 | AMD | 7 nm | 74 mm2 | 51,350,000 |
| AMD Zen 2 client I/O die | 2,090,000,000[134] | 2019 | AMD | 12 nm | 125 mm2 | 16,720,000 |
| AMD Zen 2 server I/O die | 8,340,000,000[134] | 2019 | AMD | 12 nm | 416 mm2 | 20,050,000 |
| AMD Zen 2 Renoir die | 9,800,000,000[134] | 2019 | AMD | 7 nm | 156 mm2 | 62,820,000 |
| AMD Ryzen 7 3700X (64-bit, SIMD, caches, I/O die) | 5,990,000,000[135][g] | 2019 | AMD | 7 & 12 nm (TSMC) |
199 (74+125) mm2 |
30,100,000 |
| HiSilicon Kirin 990 4G | 8,000,000,000[136] | 2019 | Huawei | 7 nm | 90.00 mm2 | 89,000,000 |
| Apple A13 (hexa-core 64-bit ARM64 "mobile SoC", SIMD, caches) | 8,500,000,000 [137][138] |
2019 | Apple | 7 nm | 98.48 mm2 | 86,300,000 |
| IBM z15 CP chip (12 cores, 256 MB L3 cache) | 9,200,000,000[139] | 2019 | IBM | 14 nm | 696 mm2 | 13,220,000 |
| IBM z15 SC chip (960 MB L4 cache) | 12,200,000,000 | 2019 | IBM | 14 nm | 696 mm2 | 17,530,000 |
| AMD Ryzen 9 3900X (64-bit, SIMD, caches, I/O die) | 9,890,000,000 [140][141] |
2019 | AMD | 7 & 12 nm (TSMC) |
273 mm2 | 36,230,000 |
| HiSilicon Kirin 990 5G | 10,300,000,000[142] | 2019 | Huawei | 7 nm | 113.31 mm2 | 90,900,000 |
| AWS Graviton2 (64-bit, 64-core ARM-based, SIMD, caches)[143][144] | 30,000,000,000 | 2019 | Amazon | 7 nm | ? | ? |
| AMD Epyc Rome (64-bit, SIMD, caches) | 39,540,000,000 [140][141] |
2019 | AMD | 7 & 12 nm (TSMC) |
1,008 mm2 | 39,226,000 |
| Qualcomm Snapdragon 865 (octa-core 64/32-bit ARM64 "mobile SoC", SIMD, caches) | 10,300,000,000[145] | 2019 | Qualcomm | 7 nm | 83.54 mm2[146] | 123,300,000 |
| TI Jacinto TDA4VM (ARM A72, DSP, SRAM) | 3,500,000,000[147] | 2020 | Texas Instruments | 16 nm | ? | ? |
| Apple A14 Bionic (hexa-core 64-bit ARM64 "mobile SoC", SIMD, caches) | 11,800,000,000[148] | 2020 | Apple | 5 nm | 88 mm2 | 134,100,000 |
| Apple M1 (octa-core 64-bit ARM64 SoC, SIMD, caches) | 16,000,000,000[149] | 2020 | Apple | 5 nm | 119 mm2 | 134,500,000 |
| HiSilicon Kirin 9000 | 15,300,000,000 [150][151] |
2020 | Huawei | 5 nm | 114 mm2 | 134,200,000 |
| AMD Zen 3 CCX (core complex unit: 8 cores, 32 MB L3 cache) | 4,080,000,000[152] | 2020 | AMD | 7 nm | 68 mm2 | 60,000,000 |
| AMD Zen 3 CCD (core complex die) | 4,150,000,000[152] | 2020 | AMD | 7 nm | 81 mm2 | 51,230,000 |
| Core 11th gen Rocket Lake (8-core 64-bit, SIMD, large caches) | 6,000,000,000+ [153] | 2021 | Intel | 14 nm +++ 14 nm | 276 mm2[154] | 37,500,000 or 21,800,000+ [155] |
| AMD Ryzen 7 5800H (64-bit, SIMD, caches, I/O and GPU) | 10,700,000,000[156] | 2021 | AMD | 7 nm | 180 mm2 | 59,440,000 |
| AMD Epyc 7763 (Milan) (64-core, 64-bit) | ? | 2021 | AMD | 7 & 12 nm (TSMC) |
1,064 mm2 (8×81+416)[157] |
? |
| Apple A15 | 15,000,000,000 [158][159] |
2021 | Apple | 5 nm | 107.68 mm2 | 139,300,000 |
| Apple M1 Pro (10-core, 64-bit) | 33,700,000,000[160] | 2021 | Apple | 5 nm | 245 mm2[161] | 137,600,000 |
| Apple M1 Max (10-core, 64-bit) | 57,000,000,000 [162][160] |
2021 | Apple | 5 nm | 420.2 mm2[163] | 135,600,000 |
| Power10 dual-chip module (30 SMT8 cores or 60 SMT4 cores) | 36,000,000,000[164] | 2021 | IBM | 7 nm | 1,204 mm2 | 29,900,000 |
| Dimensity 9000 (ARM64 SoC) | 15,300,000,000 [165][166] |
2021 | Mediatek | 4 nm (TSMC N4) |
? | ? |
| Apple A16 (ARM64 SoC) | 16,000,000,000 [167][168][169] |
2022 | Apple | 4 nm | ? | ? |
| Apple M1 Ultra (dual-chip module, 2×10 cores) | 114,000,000,000 [170][171] |
2022 | Apple | 5 nm | 840.5 mm2[163] | 135,600,000 |
| AMD Epyc 7773X (Milan-X) (multi-chip module, 64 cores, 768 MB L3 cache) | 26,000,000,000 + Milan[172] | 2022 | AMD | 7 & 12 nm (TSMC) |
1,352 mm2 (Milan + 8×36)[172] |
? |
| IBM Telum dual-chip module (2×8 cores, 2×256 MB cache) | 45,000,000,000 [173][174] |
2022 | IBM | 7 nm (Samsung) | 1,060 mm2 | 42,450,000 |
| Apple M2 (octa-core 64-bit ARM64 SoC, SIMD, caches) | 20,000,000,000[175] | 2022 | Apple | 5 nm | ? | ? |
| Dimensity 9200 (ARM64 SoC) | 17,000,000,000 [176][177][178] |
2022 | Mediatek | 4 nm (TSMC N4P) |
? | ? |
| Qualcomm Snapdragon 8 Gen 2 (octa-core ARM64 "mobile SoC", SIMD, caches) | 16,000,000,000 | 2022 | Qualcomm | 4 nm | 268 mm2 | 59,701,492 |
| AMD EPYC Genoa (4th gen/9004 series) 13-chip module (up to 96 cores and 384 MB (L3) + 96 MB (L2) cache)[179] | 90,000,000,000 [180][181] |
2022 | AMD | 5 nm (CCD) 6 nm (IOD) |
1,263.34 mm2 12×72.225 (CCD) 396.64 (IOD) [182][183] |
71,240,000 |
| HiSilicon Kirin 9000s | 9,510,000,000[184] | 2023 | Huawei | 7 nm | 107 mm2 | 107,690,000 |
| Apple M4 (deca-core 64-bit ARM64 SoC, SIMD, caches) | 28,000,000,000[185] | 2024 | Apple | 3 nm | ? | ? |
| Apple M3 (octa-core 64-bit ARM64 SoC, SIMD, caches) | 25,000,000,000[186] | 2023 | Apple | 3 nm | ? | ? |
| Apple M3 Pro (dodeca-core 64-bit ARM64 SoC, SIMD, caches) | 37,000,000,000[186] | 2023 | Apple | 3 nm | ? | ? |
| Apple M3 Max (16-core 64-bit ARM64 SoC, SIMD, caches) | 92,000,000,000[186] | 2023 | Apple | 3 nm | ? | ? |
| Apple A17 | 19,000,000,000 [187] |
2023 | Apple | 3 nm | 103.8 mm2 | 183,044,315 |
| Sapphire Rapids quad-chip module (up to 60 cores and 112.5 MB of cache)[188] | 44,000,000,000– 48,000,000,000[189] |
2023 | Intel | 10 nm ESF (Intel 7) | 1,600 mm2 | 27,500,000– 30,000,000 |
| Apple M2 Pro (12-core 64-bit ARM64 SoC, SIMD, caches) | 40,000,000,000[190] | 2023 | Apple | 5 nm | ? | ? |
| Apple M2 Max (12-core 64-bit ARM64 SoC, SIMD, caches) | 67,000,000,000[190] | 2023 | Apple | 5 nm | ? | ? |
| Apple M2 Ultra (two M2 Max dies) | 134,000,000,000[191] | 2023 | Apple | 5 nm | ? | ? |
| AMD Epyc Bergamo (4th gen/97X4 series) 9-chip module (up to 128 cores and 256 MB (L3) + 128 MB (L2) cache) | 82,000,000,000[192] | 2023 | AMD | 5 nm (CCD) 6 nm (IOD) |
? | ? |
| AMD Instinct MI300A (multi-chip module, 24 cores, 128 GB GPU memory + 256 MB (LLC/L3) cache) | 146,000,000,000[193][194] | 2023 | AMD | 5 nm (CCD, GCD) 6 nm (IOD) |
1,017 mm2 | 144,000,000 |
| RV32-WUJI: 3-atom-thick molybdenum disulfide on sapphire; RISC-V architecture | 5931[195] | 2025 | ? | 3000 nm | ? | ? |
| Processor | Transistor count | Year | Designer | Process (nm) |
Area (mm2) | Transistor density (tr./mm2) |
GPUs
[edit]A graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the building of images in a frame buffer intended for output to a display.
The designer refers to the technology company that designs the logic of the integrated circuit chip (such as Nvidia and AMD). The manufacturer ("Fab.") refers to the semiconductor company that fabricates the chip using its semiconductor manufacturing process at a foundry (such as TSMC and Samsung Semiconductor). The transistor count in a chip is dependent on a manufacturer's fabrication process, with smaller semiconductor nodes typically enabling higher transistor density and thus higher transistor counts.
The random-access memory (RAM) that comes with GPUs (such as VRAM, SGRAM or HBM) greatly increases the total transistor count, with the memory typically accounting for the majority of transistors in a graphics card. For example, Nvidia's Tesla P100 has 15 billion FinFETs (16 nm) in the GPU in addition to 16 GB of HBM2 memory, totaling about 150 billion MOSFETs on the graphics card.[196] The following table does not include the memory. For memory transistor counts, see the Memory section below.
| Processor | Transistor count | Year | Designer(s) | Fab(s) | Process | Area | Transistor density (tr./mm2) |
Ref |
|---|---|---|---|---|---|---|---|---|
| μPD7220 GDC | 40,000 | 1982 | NEC | NEC | 5,000 nm | ? | ? | [197] |
| ARTC HD63484 | 60,000 | 1984 | Hitachi | Hitachi | ? | ? | ? | [198] |
| CBM Agnus | 21,000 | 1985 | Commodore | CSG | 5,000 nm | ? | ? | [199][200] |
| YM7101 VDP | 100,000 | 1988 | Yamaha, Sega | Yamaha | ? | ? | ? | [201] |
| Tom & Jerry | 750,000 | 1993 | Flare | IBM | ? | ? | ? | [201] |
| VDP1 | 1,000,000 | 1994 | Sega | Hitachi | 500 nm | ? | ? | [202] |
| Sony GPU | 1,000,000 | 1994 | Toshiba | LSI | 500 nm | ? | ? | [203][204][205] |
| NV1 | 1,000,000 | 1995 | Nvidia, Sega | SGS | 500 nm | 90 mm2 | 11,000 | |
| Reality Coprocessor | 2,600,000 | 1996 | SGI | NEC | 350 nm | 81 mm2 | 32,100 | [206] |
| PowerVR | 1,200,000 | 1996 | VideoLogic | NEC | 350 nm | ? | ? | [207] |
| Voodoo Graphics | 1,000,000 | 1996 | 3dfx | TSMC | 500 nm | ? | ? | [208][209] |
| Voodoo Rush | 1,000,000 | 1997 | 3dfx | TSMC | 500 nm | ? | ? | [208][209] |
| NV3 | 3,500,000 | 1997 | Nvidia | SGS, TSMC | 350 nm | 90 mm2 | 38,900 | [210][211] |
| i740 | 3,500,000 | 1998 | Intel, Real3D | Real3D | 350 nm | ? | ? | [208][209] |
| Voodoo 2 | 4,000,000 | 1998 | 3dfx | TSMC | 350 nm | ? | ? | |
| Voodoo Rush | 4,000,000 | 1998 | 3dfx | TSMC | 350 nm | ? | ? | |
| NV4 | 7,000,000 | 1998 | Nvidia | TSMC | 350 nm | 90 mm2 | 78,000 | [208][211] |
| PowerVR2 CLX2 | 10,000,000 | 1998 | VideoLogic | NEC | 250 nm | 116 mm2 | 86,200 | [212][213][214][215] |
| PowerVR2 PMX1 | 6,000,000 | 1999 | VideoLogic | NEC | 250 nm | ? | ? | [216] |
| Rage 128 | 8,000,000 | 1999 | ATI | TSMC, UMC | 250 nm | 70 mm2 | 114,000 | [209] |
| Voodoo 3 | 8,100,000 | 1999 | 3dfx | TSMC | 250 nm | ? | ? | [217] |
| Graphics Synthesizer | 43,000,000 | 1999 | Sony, Toshiba | Sony, Toshiba | 180 nm | 279 mm2 | 154,000 | [65][218][64][63] |
| NV5 | 15,000,000 | 1999 | Nvidia | TSMC | 250 nm | 90 mm2 | 167,000 | [209] |
| NV10 | 17,000,000 | 1999 | Nvidia | TSMC | 220 nm | 111 mm2 | 153,000 | [219][211] |
| NV11 | 20,000,000 | 2000 | Nvidia | TSMC | 180 nm | 65 mm2 | 308,000 | [209] |
| NV15 | 25,000,000 | 2000 | Nvidia | TSMC | 180 nm | 81 mm2 | 309,000 | [209] |
| Voodoo 4 | 14,000,000 | 2000 | 3dfx | TSMC | 220 nm | ? | ? | [208][209] |
| Voodoo 5 | 28,000,000 | 2000 | 3dfx | TSMC | 220 nm | ? | ? | [208][209] |
| R100 | 30,000,000 | 2000 | ATI | TSMC | 180 nm | 97 mm2 | 309,000 | [209] |
| Flipper | 51,000,000 | 2000 | ArtX | NEC | 180 nm | 106 mm2 | 481,000 | [65][220] |
| PowerVR3 KYRO | 14,000,000 | 2001 | Imagination | ST | 250 nm | ? | ? | [208][209] |
| PowerVR3 KYRO II | 15,000,000 | 2001 | Imagination | ST | 180 nm | |||
| NV2A | 60,000,000 | 2001 | Nvidia | TSMC | 150 nm | ? | ? | [208][221] |
| NV20 | 57,000,000 | 2001 | Nvidia | TSMC | 150 nm | 128 mm2 | 445,000 | [209] |
| NV25 | 63,000,000 | 2002 | Nvidia | TSMC | 150 nm | 142 mm2 | 444,000 | |
| NV28 | 36,000,000 | 2002 | Nvidia | TSMC | 150 nm | 101 mm2 | 356,000 | |
| NV17/18 | 29,000,000 | 2002 | Nvidia | TSMC | 150 nm | 65 mm2 | 446,000 | |
| R200 | 60,000,000 | 2001 | ATI | TSMC | 150 nm | 68 mm2 | 882,000 | |
| R300 | 107,000,000 | 2002 | ATI | TSMC | 150 nm | 218 mm2 | 490,800 | |
| R360 | 117,000,000 | 2003 | ATI | TSMC | 150 nm | 218 mm2 | 536,700 | |
| NV34 | 45,000,000 | 2003 | Nvidia | TSMC | 150 nm | 124 mm2 | 363,000 | |
| NV34b | 45,000,000 | 2004 | Nvidia | TSMC | 140 nm | 91 mm2 | 495,000 | |
| NV30 | 125,000,000 | 2003 | Nvidia | TSMC | 130 nm | 199 mm2 | 628,000 | |
| NV31 | 80,000,000 | 2003 | Nvidia | TSMC | 130 nm | 121 mm2 | 661,000 | |
| NV35/38 | 135,000,000 | 2003 | Nvidia | TSMC | 130 nm | 207 mm2 | 652,000 | |
| NV36 | 82,000,000 | 2003 | Nvidia | IBM | 130 nm | 133 mm2 | 617,000 | |
| R480 | 160,000,000 | 2004 | ATI | TSMC | 130 nm | 297 mm2 | 538,700 | |
| NV40 | 222,000,000 | 2004 | Nvidia | IBM | 130 nm | 305 mm2 | 727,900 | |
| NV44 | 75,000,000 | 2004 | Nvidia | IBM | 130 nm | 110 mm2 | 681,800 | |
| NV41 | 222,000,000 | 2005 | Nvidia | TSMC | 110 nm | 225 mm2 | 986,700 | [209] |
| NV42 | 198,000,000 | 2005 | Nvidia | TSMC | 110 nm | 222 mm2 | 891,900 | |
| NV43 | 146,000,000 | 2005 | Nvidia | TSMC | 110 nm | 154 mm2 | 948,100 | |
| G70 | 303,000,000 | 2005 | Nvidia | TSMC, Chartered | 110 nm | 333 mm2 | 909,900 | |
| Xenos | 232,000,000 | 2005 | ATI | TSMC | 90 nm | 182 mm2 | 1,275,000 | [222][223] |
| RSX Reality Synthesizer | 300,000,000 | 2005 | Nvidia, Sony | Sony | 90 nm | 186 mm2 | 1,613,000 | [224][225] |
| R520 | 321,000,000 | 2005 | ATI | TSMC | 90 nm | 288 mm2 | 1,115,000 | [209] |
| RV530 | 157,000,000 | 2005 | ATI | TSMC | 90 nm | 150 mm2 | 1,047,000 | |
| RV515 | 107,000,000 | 2005 | ATI | TSMC | 90 nm | 100 mm2 | 1,070,000 | |
| R580 | 384,000,000 | 2006 | ATI | TSMC | 90 nm | 352 mm2 | 1,091,000 | |
| G71 | 278,000,000 | 2006 | Nvidia | TSMC | 90 nm | 196 mm2 | 1,418,000 | |
| G72 | 112,000,000 | 2006 | Nvidia | TSMC | 90 nm | 81 mm2 | 1,383,000 | |
| G73 | 177,000,000 | 2006 | Nvidia | TSMC | 90 nm | 125 mm2 | 1,416,000 | |
| G80 | 681,000,000 | 2006 | Nvidia | TSMC | 90 nm | 480 mm2 | 1,419,000 | |
| G86 Tesla | 210,000,000 | 2007 | Nvidia | TSMC | 80 nm | 127 mm2 | 1,654,000 | |
| G84 Tesla | 289,000,000 | 2007 | Nvidia | TSMC | 80 nm | 169 mm2 | 1,710,000 | |
| RV560 | 330,000,000 | 2006 | ATI | TSMC | 80 nm | 230 mm2 | 1,435,000 | |
| R600 | 700,000,000 | 2007 | ATI | TSMC | 80 nm | 420 mm2 | 1,667,000 | |
| RV610 | 180,000,000 | 2007 | ATI | TSMC | 65 nm | 85 mm2 | 2,118,000 | [209] |
| RV630 | 390,000,000 | 2007 | ATI | TSMC | 65 nm | 153 mm2 | 2,549,000 | |
| G92 | 754,000,000 | 2007 | Nvidia | TSMC, UMC | 65 nm | 324 mm2 | 2,327,000 | |
| G94 Tesla | 505,000,000 | 2008 | Nvidia | TSMC | 65 nm | 240 mm2 | 2,104,000 | |
| G96 Tesla | 314,000,000 | 2008 | Nvidia | TSMC | 65 nm | 144 mm2 | 2,181,000 | |
| G98 Tesla | 210,000,000 | 2008 | Nvidia | TSMC | 65 nm | 86 mm2 | 2,442,000 | |
| GT200[226] | 1,400,000,000 | 2008 | Nvidia | TSMC | 65 nm | 576 mm2 | 2,431,000 | |
| RV620 | 181,000,000 | 2008 | ATI | TSMC | 55 nm | 67 mm2 | 2,701,000 | [209] |
| RV635 | 378,000,000 | 2008 | ATI | TSMC | 55 nm | 135 mm2 | 2,800,000 | |
| RV710 | 242,000,000 | 2008 | ATI | TSMC | 55 nm | 73 mm2 | 3,315,000 | |
| RV730 | 514,000,000 | 2008 | ATI | TSMC | 55 nm | 146 mm2 | 3,521,000 | |
| RV670 | 666,000,000 | 2008 | ATI | TSMC | 55 nm | 192 mm2 | 3,469,000 | |
| RV770 | 956,000,000 | 2008 | ATI | TSMC | 55 nm | 256 mm2 | 3,734,000 | |
| RV790 | 959,000,000 | 2008 | ATI | TSMC | 55 nm | 282 mm2 | 3,401,000 | [227][209] |
| G92b Tesla | 754,000,000 | 2008 | Nvidia | TSMC, UMC | 55 nm | 260 mm2 | 2,900,000 | [209] |
| G94b Tesla | 505,000,000 | 2008 | Nvidia | TSMC, UMC | 55 nm | 196 mm2 | 2,577,000 | |
| G96b Tesla | 314,000,000 | 2008 | Nvidia | TSMC, UMC | 55 nm | 121 mm2 | 2,595,000 | |
| GT200b Tesla | 1,400,000,000 | 2008 | Nvidia | TSMC, UMC | 55 nm | 470 mm2 | 2,979,000 | |
| GT218 Tesla | 260,000,000 | 2009 | Nvidia | TSMC | 40 nm | 57 mm2 | 4,561,000 | [209] |
| GT216 Tesla | 486,000,000 | 2009 | Nvidia | TSMC | 40 nm | 100 mm2 | 4,860,000 | |
| GT215 Tesla | 727,000,000 | 2009 | Nvidia | TSMC | 40 nm | 144 mm2 | 5,049,000 | |
| RV740 | 826,000,000 | 2009 | ATI | TSMC | 40 nm | 137 mm2 | 6,029,000 | |
| Cypress RV870 | 2,154,000,000 | 2009 | ATI | TSMC | 40 nm | 334 mm2 | 6,449,000 | |
| Juniper RV840 | 1,040,000,000 | 2009 | ATI | TSMC | 40 nm | 166 mm2 | 6,265,000 | |
| Redwood RV830 | 627,000,000 | 2010 | AMD (ATI) | TSMC | 40 nm | 104 mm2 | 6,029,000 | [209] |
| Cedar RV810 | 292,000,000 | 2010 | AMD | TSMC | 40 nm | 59 mm2 | 4,949,000 | |
| Cayman RV970 | 2,640,000,000 | 2010 | AMD | TSMC | 40 nm | 389 mm2 | 6,789,000 | |
| Barts RV940 | 1,700,000,000 | 2010 | AMD | TSMC | 40 nm | 255 mm2 | 6,667,000 | |
| Turks RV930 | 716,000,000 | 2011 | AMD | TSMC | 40 nm | 118 mm2 | 6,068,000 | |
| Caicos RV910 | 370,000,000 | 2011 | AMD | TSMC | 40 nm | 67 mm2 | 5,522,000 | |
| GF100 Fermi | 3,200,000,000 | 2010 | Nvidia | TSMC | 40 nm | 526 mm2 | 6,084,000 | [228] |
| GF110 Fermi | 3,000,000,000 | 2010 | Nvidia | TSMC | 40 nm | 520 mm2 | 5,769,000 | [228] |
| GF104 Fermi | 1,950,000,000 | 2011 | Nvidia | TSMC | 40 nm | 332 mm2 | 5,873,000 | [209] |
| GF106 Fermi | 1,170,000,000 | 2010 | Nvidia | TSMC | 40 nm | 238 mm2 | 4,916,000 | [209] |
| GF108 Fermi | 585,000,000 | 2011 | Nvidia | TSMC | 40 nm | 116 mm2 | 5,043,000 | [209] |
| GF119 Fermi | 292,000,000 | 2011 | Nvidia | TSMC | 40 nm | 79 mm2 | 3,696,000 | [209] |
| Tahiti GCN1 | 4,312,711,873 | 2011 | AMD | TSMC | 28 nm | 365 mm2 | 11,820,000 | [229] |
| Cape Verde GCN1 | 1,500,000,000 | 2012 | AMD | TSMC | 28 nm | 123 mm2 | 12,200,000 | [209] |
| Pitcairn GCN1 | 2,800,000,000 | 2012 | AMD | TSMC | 28 nm | 212 mm2 | 13,210,000 | [209] |
| GK110 Kepler | 7,080,000,000 | 2012 | Nvidia | TSMC | 28 nm | 561 mm2 | 12,620,000 | [230][231] |
| GK104 Kepler | 3,540,000,000 | 2012 | Nvidia | TSMC | 28 nm | 294 mm2 | 12,040,000 | [232] |
| GK106 Kepler | 2,540,000,000 | 2012 | Nvidia | TSMC | 28 nm | 221 mm2 | 11,490,000 | [209] |
| GK107 Kepler | 1,270,000,000 | 2012 | Nvidia | TSMC | 28 nm | 118 mm2 | 10,760,000 | [209] |
| GK208 Kepler | 1,020,000,000 | 2013 | Nvidia | TSMC | 28 nm | 79 mm2 | 12,910,000 | [209] |
| Oland GCN1 | 1,040,000,000 | 2013 | AMD | TSMC | 28 nm | 90 mm2 | 11,560,000 | [209] |
| Bonaire GCN2 | 2,080,000,000 | 2013 | AMD | TSMC | 28 nm | 160 mm2 | 13,000,000 | |
| Durango (Xbox One) | 4,800,000,000 | 2013 | AMD | TSMC | 28 nm | 375 mm2 | 12,800,000 | [233][234] |
| Liverpool (PlayStation 4) | ? | 2013 | AMD | TSMC | 28 nm | 348 mm2 | ? | [235] |
| Hawaii GCN2 | 6,300,000,000 | 2013 | AMD | TSMC | 28 nm | 438 mm2 | 14,380,000 | [209] |
| GM200 Maxwell | 8,000,000,000 | 2015 | Nvidia | TSMC | 28 nm | 601 mm2 | 13,310,000 | |
| GM204 Maxwell | 5,200,000,000 | 2014 | Nvidia | TSMC | 28 nm | 398 mm2 | 13,070,000 | |
| GM206 Maxwell | 2,940,000,000 | 2014 | Nvidia | TSMC | 28 nm | 228 mm2 | 12,890,000 | |
| GM107 Maxwell | 1,870,000,000 | 2014 | Nvidia | TSMC | 28 nm | 148 mm2 | 12,640,000 | |
| Tonga GCN3 | 5,000,000,000 | 2014 | AMD | TSMC, GlobalFoundries | 28 nm | 366 mm2 | 13,660,000 | |
| Fiji GCN3 | 8,900,000,000 | 2015 | AMD | TSMC | 28 nm | 596 mm2 | 14,930,000 | |
| Durango 2 (Xbox One S) | 5,000,000,000 | 2016 | AMD | TSMC | 16 nm | 240 mm2 | 20,830,000 | [236] |
| Neo (PlayStation 4 Pro) | 5,700,000,000 | 2016 | AMD | TSMC | 16 nm | 325 mm2 | 17,540,000 | [237] |
| Ellesmere/Polaris 10 GCN4 | 5,700,000,000 | 2016 | AMD | Samsung, GlobalFoundries | 14 nm | 232 mm2 | 24,570,000 | [238] |
| Baffin/Polaris 11 GCN4 | 3,000,000,000 | 2016 | AMD | Samsung, GlobalFoundries | 14 nm | 123 mm2 | 24,390,000 | [209][239] |
| Lexa/Polaris 12 GCN4 | 2,200,000,000 | 2017 | AMD | Samsung, GlobalFoundries | 14 nm | 101 mm2 | 21,780,000 | [209][239] |
| GP100 Pascal | 15,300,000,000 | 2016 | Nvidia | TSMC, Samsung | 16 nm | 610 mm2 | 25,080,000 | [240][241] |
| GP102 Pascal | 11,800,000,000 | 2016 | Nvidia | TSMC, Samsung | 16 nm | 471 mm2 | 25,050,000 | [209][241] |
| GP104 Pascal | 7,200,000,000 | 2016 | Nvidia | TSMC | 16 nm | 314 mm2 | 22,930,000 | [209][241] |
| GP106 Pascal | 4,400,000,000 | 2016 | Nvidia | TSMC | 16 nm | 200 mm2 | 22,000,000 | [209][241] |
| GP107 Pascal | 3,300,000,000 | 2016 | Nvidia | Samsung | 14 nm | 132 mm2 | 25,000,000 | [209][241] |
| GP108 Pascal | 1,850,000,000 | 2017 | Nvidia | Samsung | 14 nm | 74 mm2 | 25,000,000 | [209][241] |
| Scorpio (Xbox One X) | 6,600,000,000 | 2017 | AMD | TSMC | 16 nm | 367 mm2 | 17,980,000 | [233][242] |
| Vega 10 GCN5 | 12,500,000,000 | 2017 | AMD | Samsung, GlobalFoundries | 14 nm | 484 mm2 | 25,830,000 | [243] |
| GV100 Volta | 21,100,000,000 | 2017 | Nvidia | TSMC | 12 nm | 815 mm2 | 25,890,000 | [244] |
| TU102 Turing | 18,600,000,000 | 2018 | Nvidia | TSMC | 12 nm | 754 mm2 | 24,670,000 | [245] |
| TU104 Turing | 13,600,000,000 | 2018 | Nvidia | TSMC | 12 nm | 545 mm2 | 24,950,000 | |
| TU106 Turing | 10,800,000,000 | 2018 | Nvidia | TSMC | 12 nm | 445 mm2 | 24,270,000 | |
| TU116 Turing | 6,600,000,000 | 2019 | Nvidia | TSMC | 12 nm | 284 mm2 | 23,240,000 | [246] |
| TU117 Turing | 4,700,000,000 | 2019 | Nvidia | TSMC | 12 nm | 200 mm2 | 23,500,000 | [247] |
| Vega 20 GCN5 | 13,230,000,000 | 2018 | AMD | TSMC | 7 nm | 331 mm2 | 39,970,000 | [209] |
| Navi 10 RDNA | 10,300,000,000 | 2019 | AMD | TSMC | 7 nm | 251 mm2 | 41,040,000 | [248] |
| Navi 12 RDNA | ? | 2020 | AMD | TSMC | 7 nm | ? | ? | |
| Navi 14 RDNA | 6,400,000,000 | 2019 | AMD | TSMC | 7 nm | 158 mm2 | 40,510,000 | [249] |
| Arcturus CDNA | 25,600,000,000 | 2020 | AMD | TSMC | 7 nm | 750 mm2 | 34,100,000 | [250] |
| GA100 Ampere | 54,200,000,000 | 2020 | Nvidia | TSMC | 7 nm | 826 mm2 | 65,620,000 | [251][252] |
| GA102 Ampere | 28,300,000,000 | 2020 | Nvidia | Samsung | 8 nm | 628 mm2 | 45,035,000 | [253][254] |
| GA103 Ampere | 22,000,000,000 | 2022 | Nvidia | Samsung | 8 nm | 496 mm2 | 44,400,000 | [255] |
| GA104 Ampere | 17,400,000,000 | 2020 | Nvidia | Samsung | 8 nm | 392 mm2 | 44,390,000 | [256] |
| GA106 Ampere | 12,000,000,000 | 2021 | Nvidia | Samsung | 8 nm | 276 mm2 | 43,480,000 | [257] |
| GA107 Ampere | 8,700,000,000 | 2021 | Nvidia | Samsung | 8 nm | 200 mm2 | 43,500,000 | [258] |
| Navi 21 RDNA2 | 26,800,000,000 | 2020 | AMD | TSMC | 7 nm | 520 mm2 | 51,540,000 | |
| Navi 22 RDNA2 | 17,200,000,000 | 2021 | AMD | TSMC | 7 nm | 335 mm2 | 51,340,000 | |
| Navi 23 RDNA2 | 11,060,000,000 | 2021 | AMD | TSMC | 7 nm | 237 mm2 | 46,670,000 | |
| Navi 24 RDNA2 | 5,400,000,000 | 2022 | AMD | TSMC | 6 nm | 107 mm2 | 50,470,000 | |
| Aldebaran CDNA2 | 58,200,000,000 (MCM) | 2021 | AMD | TSMC | 6 nm | 1448–1474 mm2[259] 1480 mm2[260] 1490–1580 mm2[261] |
39,500,000–40,200,000 39,200,000 36,800,000–39,100,000 |
[262] |
| GH100 Hopper | 80,000,000,000 | 2022 | Nvidia | TSMC | 4 nm | 814 mm2 | 98,280,000 | [263] |
| AD102 Ada Lovelace | 76,300,000,000 | 2022 | Nvidia | TSMC | 4 nm | 608.4 mm2 | 125,411,000 | [264] |
| AD103 Ada Lovelace | 45,900,000,000 | 2022 | Nvidia | TSMC | 4 nm | 378.6 mm2 | 121,240,000 | [265] |
| AD104 Ada Lovelace | 35,800,000,000 | 2022 | Nvidia | TSMC | 4 nm | 294.5 mm2 | 121,560,000 | [265] |
| AD106 Ada Lovelace | ? | 2023 | Nvidia | TSMC | 4 nm | 190 mm2 | ? | [266][267] |
| AD107 Ada Lovelace | ? | 2023 | Nvidia | TSMC | 4 nm | 146 mm2 | ? | [266][268] |
| Navi 31 RDNA3 | 57,700,000,000 (MCM) 45,400,000,000 (GCD) 6×2,050,000,000 (MCD) |
2022 | AMD | TSMC | 5 nm (GCD) 6 nm (MCD) |
531 mm2 (MCM) 306 mm2 (GCD) 6×37.5 mm2 (MCD) |
109,200,000 (MCM) 132,400,000 (GCD) 54,640,000 (MCD) |
[269][270][271] |
| Navi 32 RDNA3 | 28,100,000,000 (MCM) | 2023 | AMD | TSMC | 5 nm (GCD) 6 nm (MCD) |
350 mm2 (MCM) 200 mm2 (GCD) 4×37.5 mm2 (MCD) |
80,200,000 (MCM) | [272] |
| Navi 33 RDNA3 | 13,300,000,000 | 2023 | AMD | TSMC | 6 nm | 204 mm2 | 65,200,000 | [273] |
| Aqua Vanjaram CDNA3 | 153,000,000,000 (MCM) | 2023 | AMD | TSMC | 5 nm (GCD) 6 nm (MCD) |
? | ? | [274][275] |
| GB200 Grace Blackwell | 208,000,000,000 (MCM) | 2024 | Nvidia | TSMC | 4 nm | ? | ? | [276] |
| GB202 Blackwell | 92,200,000,000 | 2025 | Nvidia | TSMC | 4 nm | 750 mm2 | 122,600,000 | [277] |
| GB203 Blackwell | 45,600,000,000 | 2025 | Nvidia | TSMC | 4 nm | 378 mm2 | 120,600,000 | [278] |
| GB205 Blackwell | 31,100,000,000 | 2025 | Nvidia | TSMC | 4 nm | 263 mm2 | 118,300,000 | [279] |
| GB206 Blackwell | 21,900,000,000 | 2025 | Nvidia | TSMC | 4 nm | 181 mm2 | 121,000,000 | [280] |
| GB207 Blackwell | 16,900,000,000 | 2025 | Nvidia | TSMC | 4 nm | 149 mm2 | 113,400,000 | [281] |
| Navi 44 RDNA4 | 29,700,000,000 | 2025 | AMD | TSMC | 4 nm | 199 mm2 | 149,200,000 | [282] |
| Navi 48 RDNA4 | 53,900,000,000 | 2025 | AMD | TSMC | 4 nm | 357 mm2 | 151,000,000 | [283] |
| Processor | Transistor count | Year | Designer(s) | Fab(s) | MOS process | Area | Transistor density (tr./mm2) |
Ref |
FPGA
[edit]A field-programmable gate array (FPGA) is an integrated circuit designed to be configured by a customer or a designer after manufacturing.
| FPGA | Transistor count | Date of introduction | Designer | Manufacturer | Process | Area | Transistor density, tr./mm2 | Ref |
|---|---|---|---|---|---|---|---|---|
| Virtex | 70,000,000 | 1997 | Xilinx | |||||
| Virtex-E | 200,000,000 | 1998 | Xilinx | |||||
| Virtex-II | 350,000,000 | 2000 | Xilinx | 130 nm | ||||
| Virtex-II PRO | 430,000,000 | 2002 | Xilinx | |||||
| Virtex-4 | 1,000,000,000 | 2004 | Xilinx | 90 nm | ||||
| Virtex-5 | 1,100,000,000 | 2006 | Xilinx | TSMC | 65 nm | [284] | ||
| Stratix IV | 2,500,000,000 | 2008 | Altera | TSMC | 40 nm | [285] | ||
| Stratix V | 3,800,000,000 | 2011 | Altera | TSMC | 28 nm | [citation needed] | ||
| Arria 10 | 5,300,000,000 | 2014 | Altera | TSMC | 20 nm | [286] | ||
| Virtex-7 2000T | 6,800,000,000 | 2011 | Xilinx | TSMC | 28 nm | [287] | ||
| Stratix 10 SX 2800 | 17,000,000,000 | TBD | Intel | Intel | 14 nm | 560 mm2 | 30,400,000 | [288][289] |
| Virtex-Ultrascale VU440 | 20,000,000,000 | Q1 2015 | Xilinx | TSMC | 20 nm | [290][291] | ||
| Virtex-Ultrascale+ VU19P | 35,000,000,000 | 2020 | Xilinx | TSMC | 16 nm | 900 mm2[h] | 38,900,000 | [292][293][294] |
| Versal VC1902 | 37,000,000,000 | 2H 2019 | Xilinx | TSMC | 7 nm | [295][296][297] | ||
| Stratix 10 GX 10M | 43,300,000,000 | Q4 2019 | Intel | Intel | 14 nm | 1,400 mm2[h] | 30,930,000 | [298][299] |
| Versal VP1802 | 92,000,000,000 | 2021 ?[i] | Xilinx | TSMC | 7 nm | [300][301] |
Memory
[edit]Semiconductor memory is an electronic data storage device, often used as computer memory, implemented on integrated circuits. Nearly all semiconductor memories since the 1970s have used MOSFETs (MOS transistors), replacing earlier bipolar junction transistors. There are two major types of semiconductor memory: random-access memory (RAM) and non-volatile memory (NVM). In turn, there are two major RAM types: dynamic random-access memory (DRAM) and static random-access memory (SRAM), as well as two major NVM types: flash memory and read-only memory (ROM).
Typical CMOS SRAM consists of six transistors per cell. For DRAM, 1T1C, which means one transistor and one capacitor structure, is common. Capacitor charged or not[clarification needed] is used to store 1 or 0. In flash memory, the data is stored in floating gates, and the resistance of the transistor is sensed[clarification needed] to interpret the data stored. Depending on how fine scale the resistance could be separated[clarification needed], one transistor could store up to three bits, meaning eight distinctive levels of resistance possible per transistor. However, a finer scale comes with the cost of repeatability issues, and hence reliability. Typically, low grade 2-bits MLC flash is used for flash drives, so a 16 GB flash drive contains roughly 64 billion transistors.
For SRAM chips, six-transistor cells (six transistors per bit) was the standard.[302] DRAM chips during the early 1970s had three-transistor cells (three transistors per bit), before single-transistor cells (one transistor per bit) became standard since the era of 4 Kb DRAM in the mid-1970s.[303][304] In single-level flash memory, each cell contains one floating-gate MOSFET (one transistor per bit),[305] whereas multi-level flash contains 2, 3 or 4 bits per transistor.
Flash memory chips are commonly stacked up in layers, up to 128-layer in production,[306] and 136-layer managed,[307] and available in end-user devices up to 69-layer from manufacturers.
| Chip name | Capacity (bits) | RAM type | Transistor count | Date of introduction | Manufacturer(s) | Process | Area | Transistor density (tr./mm2) |
Ref |
|---|---|---|---|---|---|---|---|---|---|
| — | 1-bit | SRAM (cell) | 6 | 1963 | Fairchild | — | — | ? | [308] |
| — | 1-bit | DRAM (cell) | 1 | 1965 | Toshiba | — | — | ? | [309][310] |
| ? | 8-bit | SRAM (bipolar) | 48 | 1965 | SDS, Signetics | ? | ? | ? | [308] |
| SP95 | 16-bit | SRAM (bipolar) | 80 | 1965 | IBM | ? | ? | ? | [311] |
| TMC3162 | 16-bit | SRAM (TTL) | 96 | 1966 | Transitron | — | ? | ? | [304] |
| ? | ? | SRAM (MOS) | ? | 1966 | NEC | ? | ? | ? | [303] |
| 256-bit | DRAM (IC) | 256 | 1968 | Fairchild | ? | ? | ? | [304] | |
| 64-bit | SRAM (PMOS) | 384 | 1968 | Fairchild | ? | ? | ? | [303] | |
| 144-bit | SRAM (NMOS) | 864 | 1968 | NEC | |||||
| 1101 | 256-bit | SRAM (PMOS) | 1,536 | 1969 | Intel | 12,000 nm | ? | ? | [312][313][314] |
| 1102 | 1 Kb | DRAM (PMOS) | 3,072 | 1970 | Intel, Honeywell | ? | ? | ? | [303] |
| 1103 | 1 Kb | DRAM (PMOS) | 3,072 | 1970 | Intel | 8,000 nm | 10 mm2 | 307 | [315][302][316][304] |
| μPD403 | 1 Kb | DRAM (NMOS) | 3,072 | 1971 | NEC | ? | ? | ? | [317] |
| ? | 2 Kb | DRAM (PMOS) | 6,144 | 1971 | General Instrument | ? | 12.7 mm2 | 484 | [318] |
| 2102 | 1 Kb | SRAM (NMOS) | 6,144 | 1972 | Intel | ? | ? | ? | [312][319] |
| ? | 8 Kb | DRAM (PMOS) | 8,192 | 1973 | IBM | ? | 18.8 mm2 | 436 | [318] |
| 5101 | 1 Kb | SRAM (CMOS) | 6,144 | 1974 | Intel | ? | ? | ? | [312] |
| 2116 | 16 Kb | DRAM (NMOS) | 16,384 | 1975 | Intel | ? | ? | ? | [320][304] |
| 2114 | 4 Kb | SRAM (NMOS) | 24,576 | 1976 | Intel | ? | ? | ? | [312][321] |
| ? | 4 Kb | SRAM (CMOS) | 24,576 | 1977 | Toshiba | ? | ? | ? | [313] |
| 64 Kb | DRAM (NMOS) | 65,536 | 1977 | NTT | ? | 35.4 mm2 | 1851 | [318] | |
| DRAM (VMOS) | 65,536 | 1979 | Siemens | ? | 25.2 mm2 | 2601 | [318] | ||
| 16 Kb | SRAM (CMOS) | 98,304 | 1980 | Hitachi, Toshiba | ? | ? | ? | [322] | |
| 256 Kb | DRAM (NMOS) | 262,144 | 1980 | NEC | 1,500 nm | 41.6 mm2 | 6302 | [318] | |
| NTT | 1,000 nm | 34.4 mm2 | 7620 | [318] | |||||
| 64 Kb | SRAM (CMOS) | 393,216 | 1980 | Matsushita | ? | ? | ? | [322] | |
| 288 Kb | DRAM | 294,912 | 1981 | IBM | ? | 25 mm2 | 11,800 | [323] | |
| 64 Kb | SRAM (NMOS) | 393,216 | 1982 | Intel | 1,500 nm | ? | ? | [322] | |
| 256 Kb | SRAM (CMOS) | 1,572,864 | 1984 | Toshiba | 1,200 nm | ? | ? | [322][314] | |
| 8 Mb | DRAM | 8,388,608 | January 5, 1984 | Hitachi | ? | ? | ? | [324][325] | |
| 16 Mb | DRAM (CMOS) | 16,777,216 | 1987 | NTT | 700 nm | 148 mm2 | 113,400 | [318] | |
| 4 Mb | SRAM (CMOS) | 25,165,824 | 1990 | NEC, Toshiba, Hitachi, Mitsubishi | ? | ? | ? | [322] | |
| 64 Mb | DRAM (CMOS) | 67,108,864 | 1991 | Matsushita, Mitsubishi, Fujitsu, Toshiba | 400 nm | ||||
| KM48SL2000 | 16 Mb | SDRAM | 16,777,216 | 1992 | Samsung | ? | ? | ? | [326][327] |
| ? | 16 Mb | SRAM (CMOS) | 100,663,296 | 1992 | Fujitsu, NEC | 400 nm | ? | ? | [322] |
| 256 Mb | DRAM (CMOS) | 268,435,456 | 1993 | Hitachi, NEC | 250 nm | ||||
| 1 Gb | DRAM | 1,073,741,824 | January 9, 1995 | NEC | 250 nm | ? | ? | [328][329] | |
| Hitachi | 160 nm | ? | ? | ||||||
| SDRAM | 1,073,741,824 | 1996 | Mitsubishi | 150 nm | ? | ? | [322] | ||
| SDRAM (SOI) | 1,073,741,824 | 1997 | Hyundai | ? | ? | ? | [330] | ||
| 4 Gb | DRAM (4-bit) | 1,073,741,824 | 1997 | NEC | 150 nm | ? | ? | [322] | |
| DRAM | 4,294,967,296 | 1998 | Hyundai | ? | ? | ? | [330] | ||
| 8 Gb | SDRAM (DDR3) | 8,589,934,592 | April 2008 | Samsung | 50 nm | ? | ? | [331] | |
| 16 Gb | SDRAM (DDR3) | 17,179,869,184 | 2008 | ||||||
| 32 Gb | SDRAM (HBM2) | 34,359,738,368 | 2016 | Samsung | 20 nm | ? | ? | [332] | |
| 64 Gb | SDRAM (HBM2) | 68,719,476,736 | 2017 | ||||||
| 128 Gb | SDRAM (DDR4) | 137,438,953,472 | 2018 | Samsung | 10 nm | ? | ? | [333] | |
| ? | RRAM[334] (3DSoC)[335] | ? | 2019 | SkyWater Technology[336] | 90 nm | ? | ? |
| Chip name | Capacity (bits) | Flash type | FGMOS transistor count | Date of introduction | Manufacturer(s) | Process | Area | Transistor density (tr./mm2) |
Ref |
|---|---|---|---|---|---|---|---|---|---|
| ? | 256 Kb | NOR | 262,144 | 1985 | Toshiba | 2,000 nm | ? | ? | [322] |
| 1 Mb | NOR | 1,048,576 | 1989 | Seeq, Intel | ? | ||||
| 4 Mb | NAND | 4,194,304 | 1989 | Toshiba | 1,000 nm | ||||
| 16 Mb | NOR | 16,777,216 | 1991 | Mitsubishi | 600 nm | ||||
| DD28F032SA | 32 Mb | NOR | 33,554,432 | 1993 | Intel | ? | 280 mm2 | 120,000 | [312][337] |
| ? | 64 Mb | NOR | 67,108,864 | 1994 | NEC | 400 nm | ? | ? | [322] |
| NAND | 67,108,864 | 1996 | Hitachi | ||||||
| 128 Mb | NAND | 134,217,728 | 1996 | Samsung, Hitachi | ? | ||||
| 256 Mb | NAND | 268,435,456 | 1999 | Hitachi, Toshiba | 250 nm | ||||
| 512 Mb | NAND | 536,870,912 | 2000 | Toshiba | ? | ? | ? | [338] | |
| 1 Gb | 2-bit NAND | 536,870,912 | 2001 | Samsung | ? | ? | ? | [322] | |
| Toshiba, SanDisk | 160 nm | ? | ? | [339] | |||||
| 2 Gb | NAND | 2,147,483,648 | 2002 | Samsung, Toshiba | ? | ? | ? | [340][341] | |
| 8 Gb | NAND | 8,589,934,592 | 2004 | Samsung | 60 nm | ? | ? | [340] | |
| 16 Gb | NAND | 17,179,869,184 | 2005 | Samsung | 50 nm | ? | ? | [342] | |
| 32 Gb | NAND | 34,359,738,368 | 2006 | Samsung | 40 nm | ||||
| THGAM | 128 Gb | Stacked NAND | 128,000,000,000 | April 2007 | Toshiba | 56 nm | 252 mm2 | 507,900,000 | [343] |
| THGBM | 256 Gb | Stacked NAND | 256,000,000,000 | 2008 | Toshiba | 43 nm | 353 mm2 | 725,200,000 | [344] |
| THGBM2 | 1 Tb | Stacked 4-bit NAND | 256,000,000,000 | 2010 | Toshiba | 32 nm | 374 mm2 | 684,500,000 | [345] |
| KLMCG8GE4A | 512 Gb | Stacked 2-bit NAND | 256,000,000,000 | 2011 | Samsung | ? | 192 mm2 | 1,333,000,000 | [346] |
| KLUFG8R1EM | 4 Tb | Stacked 3-bit V-NAND | 1,365,333,333,504 | 2017 | Samsung | ? | 150 mm2 | 9,102,000,000 | [347] |
| eUFS (1 TB) | 8 Tb | Stacked 4-bit V-NAND | 2,048,000,000,000 | 2019 | Samsung | ? | 150 mm2 | 13,650,000,000 | [348][349] |
| ? | 1 Tb | 232L TLC NAND die | 333,333,333,333 | 2022 | Micron | ? | 68.5 mm2 (memory array) |
4,870,000,000 (14.6 Gbit/mm2) |
[350][351][352][353] |
| ? | 16 Tb | 232L package | 5,333,333,333,333 | 2022 | Micron | ? | 68.5 mm2 (memory array) |
77,900,000,000 (16×14.6 Gbit/mm2) |
| Chip name | Capacity (bits) | ROM type | Transistor count | Date of introduction | Manufacturer(s) | Process | Area | Ref |
|---|---|---|---|---|---|---|---|---|
| ? | ? | PROM | ? | 1956 | Arma | — | ? | [354][355] |
| 1 Kb | ROM (MOS) | 1,024 | 1965 | General Microelectronics | ? | ? | [356] | |
| 3301 | 1 Kb | ROM (bipolar) | 1,024 | 1969 | Intel | — | ? | [356] |
| 1702 | 2 Kb | EPROM (MOS) | 2,048 | 1971 | Intel | ? | 15 mm2 | [357] |
| ? | 4 Kb | ROM (MOS) | 4,096 | 1974 | AMD, General Instrument | ? | ? | [356] |
| 2708 | 8 Kb | EPROM (MOS) | 8,192 | 1975 | Intel | ? | ? | [312] |
| ? | 2 Kb | EEPROM (MOS) | 2,048 | 1976 | Toshiba | ? | ? | [358] |
| μCOM-43 ROM | 16 Kb | PROM (PMOS) | 16,000 | 1977 | NEC | ? | ? | [359] |
| 2716 | 16 Kb | EPROM (TTL) | 16,384 | 1977 | Intel | — | ? | [315][360] |
| EA8316F | 16 Kb | ROM (NMOS) | 16,384 | 1978 | Electronic Arrays | ? | 436 mm2 | [356][361] |
| 2732 | 32 Kb | EPROM | 32,768 | 1978 | Intel | ? | ? | [312] |
| 2364 | 64 Kb | ROM | 65,536 | 1978 | Intel | ? | ? | [362] |
| 2764 | 64 Kb | EPROM | 65,536 | 1981 | Intel | 3,500 nm | ? | [312][322] |
| 27128 | 128 Kb | EPROM | 131,072 | 1982 | Intel | ? | ||
| 27256 | 256 Kb | EPROM (HMOS) | 262,144 | 1983 | Intel | ? | ? | [312][363] |
| ? | 256 Kb | EPROM (CMOS) | 262,144 | 1983 | Fujitsu | ? | ? | [364] |
| 512 Kb | EPROM (NMOS) | 524,288 | 1984 | AMD | 1,700 nm | ? | [322] | |
| 27512 | 512 Kb | EPROM (HMOS) | 524,288 | 1984 | Intel | ? | ? | [312][365] |
| ? | 1 Mb | EPROM (CMOS) | 1,048,576 | 1984 | NEC | 1,200 nm | ? | [322] |
| 4 Mb | EPROM (CMOS) | 4,194,304 | 1987 | Toshiba | 800 nm | |||
| 16 Mb | EPROM (CMOS) | 16,777,216 | 1990 | NEC | 600 nm | |||
| MROM | 16,777,216 | 1995 | AKM, Hitachi | ? | ? | [329] |
Transistor computers
[edit]
Before transistors were invented, relays were used in commercial tabulating machines and experimental early computers. The world's first working programmable, fully automatic digital computer,[366] the 1941 Z3 22-bit word length computer, had 2,600 relays, and operated at a clock frequency of about 4–5 Hz. The 1940 Complex Number Computer had fewer than 500 relays,[367] but it was not fully programmable. The earliest practical computers used vacuum tubes and solid-state diode logic. ENIAC had 18,000 vacuum tubes, 7,200 crystal diodes, and 1,500 relays, with many of the vacuum tubes containing two triode elements.
The second generation of computers were transistor computers that featured boards filled with discrete transistors, solid-state diodes and magnetic memory cores. The experimental 1953 48-bit Transistor Computer, developed at the University of Manchester, is widely believed to be the first transistor computer to come into operation anywhere in the world (the prototype had 92 point-contact transistors and 550 diodes).[368] A later version the 1955 machine had a total of 250 junction transistors and 1,300 point-contact diodes. The Computer also used a small number of tubes in its clock generator, so it was not the first fully transistorized. The ETL Mark III, developed at the Electrotechnical Laboratory in 1956, may have been the first transistor-based electronic computer using the stored program method. It had about "130 point-contact transistors and about 1,800 germanium diodes were used for logic elements, and these were housed on 300 plug-in packages which could be slipped in and out."[369] The 1958 decimal architecture IBM 7070 was the first transistor computer to be fully programmable. It had about 30,000 alloy-junction germanium transistors and 22,000 germanium diodes, on approximately 14,000 Standard Modular System (SMS) cards. The 1959 MOBIDIC, short for "MOBIle DIgital Computer", at 12,000 pounds (6.0 short tons) mounted in the trailer of a semi-trailer truck, was a transistorized computer for battlefield data.
The third generation of computers used integrated circuits (ICs).[370] The 1962 15-bit Apollo Guidance Computer used "about 4,000 "Type-G" (3-input NOR gate) circuits" for about 12,000 transistors plus 32,000 resistors.[371] The IBM System/360, introduced 1964, used discrete transistors in hybrid circuit packs.[370] The 1965 12-bit PDP-8 CPU had 1409 discrete transistors and over 10,000 diodes, on many cards. Later versions, starting with the 1968 PDP-8/I, used integrated circuits. The PDP-8 was later reimplemented as a microprocessor as the Intersil 6100, see below.[372]
The next generation of computers were the microcomputers, starting with the 1971 Intel 4004, which used MOS transistors. These were used in home computers or personal computers (PCs).
This list includes early transistorized computers (second generation) and IC-based computers (third generation) from the 1950s and 1960s.
| Computer | Transistor count | Year | Manufacturer | Notes | Ref |
|---|---|---|---|---|---|
| Transistor Computer | 92 | 1953 | University of Manchester | Point-contact transistors, 550 diodes. Lacked stored program capability. | [368] |
| TRADIC | 700 | 1954 | Bell Labs | Point-contact transistors | [368] |
| Transistor Computer (full size) | 250 | 1955 | University of Manchester | Discrete point-contact transistors, 1,300 diodes | [368] |
| IBM 608 | 3,000 | 1955 | IBM | Germanium transistors | [373] |
| ETL Mark III | 130 | 1956 | Electrotechnical Laboratory | Point-contact transistors, 1,800 diodes, stored program capability | [368][369] |
| Metrovick 950 | 200 | 1956 | Metropolitan-Vickers | Discrete junction transistors | |
| NEC NEAC-2201 | 600 | 1958 | NEC | Germanium transistors | [374] |
| Hitachi MARS-1 | 1,000 | 1958 | Hitachi | [375] | |
| IBM 7070 | 30,000 | 1958 | IBM | Alloy-junction germanium transistors, 22,000 diodes | [376] |
| Matsushita MADIC-I | 400 | 1959 | Matsushita | Bipolar transistors | [377] |
| NEC NEAC-2203 | 2,579 | 1959 | NEC | [378] | |
| Toshiba TOSBAC-2100 | 5,000 | 1959 | Toshiba | [379] | |
| IBM 7090 | 50,000 | 1959 | IBM | Discrete germanium transistors | [380] |
| PDP-1 | 2,700 | 1959 | Digital Equipment Corporation | Discrete transistors | |
| Olivetti Elea 9003 | ? | 1959 | Olivetti | 300,000 (?) discrete transistors and diodes | [381] |
| Mitsubishi MELCOM 1101 | 3,500 | 1960 | Mitsubishi | Germanium transistors | [382] |
| M18 FADAC | 1,600 | 1960 | Autonetics | Discrete transistors | |
| CPU of IBM 7030 Stretch | 169,100 | 1961 | IBM | World's fastest computer from 1961 to 1964 | [383] |
| D-17B | 1,521 | 1962 | Autonetics | Discrete transistors | |
| NEC NEAC-L2 | 16,000 | 1964 | NEC | Ge transistors | [384] |
| CDC 6600 (entire computer) | 400,000 | 1964 | Control Data Corporation | World's fastest computer from 1964 to 1969 | [385] |
| IBM System/360 | ? | 1964 | IBM | Hybrid circuits | |
| PDP-8 "Straight-8" | 1,409[372] | 1965 | Digital Equipment Corporation | discrete transistors, 10,000 diodes | |
| PDP-8/S | 1,001[386][387][388] | 1966 | Digital Equipment Corporation | discrete transistors, diodes | |
| PDP-8/I | 1,409[citation needed] | 1968[389] | Digital Equipment Corporation | 74 series TTL circuits[390] | |
| Apollo Guidance Computer Block I | 12,300 | 1966 | Raytheon / MIT Instrumentation Laboratory | 4,100 ICs, each containing a 3-transistor, 3-input NOR gate. (Block II had 2,800 dual 3-input NOR gates ICs.) |
Logic functions
[edit]Transistor count for generic logic functions is based on static CMOS implementation.[391]
| Function | Transistor count | Ref. |
|---|---|---|
| NOT | 2 | |
| Buffer | 4 | |
| NAND 2-input | 4 | |
| NOR 2-input | 4 | |
| AND 2-input | 6 | |
| OR 2-input | 6 | |
| NAND 3-input | 6 | |
| NOR 3-input | 6 | |
| XOR 2-input | 6 | |
| XNOR 2-input | 8 | |
| MUX 2-input with TG | 6 | |
| MUX 4-input with TG | 18 | |
| NOT MUX 2-input | 8 | |
| MUX 4-input | 24 | |
| 1-bit full adder | 24 | |
| 1-bit adder–subtractor | 48 | |
| AND-OR-INVERT | 6 | [392] |
| Latch, D gated | 8 | |
| Flip-flop, edge triggered dynamic D with reset | 12 | |
| 8-bit multiplier | 3,000 | |
| 16-bit multiplier | 9,000 | |
| 32-bit multiplier | 21,000 | [citation needed] |
| small-scale integration | 2–100 | [393] |
| medium-scale integration | 100–500 | [393] |
| large-scale integration | 500–20,000 | [393] |
| very-large-scale integration | 20,000–1,000,000 | [393] |
| ultra-large scale integration | >1,000,000 |
Parallel systems
[edit]Historically, each processing element in earlier parallel systems—like all CPUs of that time—was a serial computer built out of multiple chips. As transistor counts per chip increases, each processing element could be built out of fewer chips, and then later each multi-core processor chip could contain more processing elements.[394]
Goodyear MPP: (1983?) 8 pixel processors per chip, 3,000 to 8,000 transistors per chip.[394]
Brunel University Scape (single-chip array-processing element): (1983) 256 pixel processors per chip, 120,000 to 140,000 transistors per chip.[394]
Cell Broadband Engine: (2006) with 9 cores per chip, had 234 million transistors per chip.[395]
Other devices
[edit]| Device type | Device name | Transistor count | Date of introduction | Designer(s) | Manufacturer(s) | MOS process | Area | Transistor density, tr./mm2 | Ref |
|---|---|---|---|---|---|---|---|---|---|
| Deep learning engine / IPU[j] | Colossus GC2 | 23,600,000,000 | 2018 | Graphcore | TSMC | 16 nm | ~800 mm2 | 29,500,000 | [396][397][398][better source needed] |
| Deep learning engine / IPU | Wafer Scale Engine | 1,200,000,000,000 | 2019 | Cerebras | TSMC | 16 nm | 46,225 mm2 | 25,960,000 | [1][2][3][4] |
| Deep learning engine / IPU | Wafer Scale Engine 2 | 2,600,000,000,000 | 2020 | Cerebras | TSMC | 7 nm | 46,225 mm2 | 56,250,000 | [5][399][400] |
| Network switch | NVLink4 NVSwitch | 25,100,000,000 | 2022 | Nvidia | TSMC | N4 (4 nm) | 294 mm2 | 85,370,000 | [401] |
Transistor density
[edit]The transistor density is the number of transistors that are fabricated per unit area, typically measured in terms of the number of transistors per square millimeter (mm2). The transistor density usually correlates with the gate length of a semiconductor node (also known as a semiconductor manufacturing process), typically measured in nanometers (nm). As of 2019[update], the semiconductor node with the highest transistor density is TSMC's 5 nanometer node, with 171.3 million transistors per square millimeter (note this corresponds to a transistor-transistor spacing of 76.4 nm, far greater than the relative meaningless "5nm")[402]
MOSFET nodes
[edit]| Node name | Transistor density (transistors/mm2) | Production year | Process | MOSFET | Manufacturer(s) | Ref |
|---|---|---|---|---|---|---|
| ? | ? | 1960 | 20,000 nm | PMOS | Bell Labs | [403][404] |
| ? | ? | 1960 | 20,000 nm | NMOS | ||
| ? | ? | 1963 | ? | CMOS | Fairchild | [405] |
| ? | ? | 1964 | ? | PMOS | General Microelectronics | [406] |
| ? | ? | 1968 | 20,000 nm | CMOS | RCA | [407] |
| ? | ? | 1969 | 12,000 nm | PMOS | Intel | [322][314] |
| ? | ? | 1970 | 10,000 nm | CMOS | RCA | [407] |
| ? | 300 | 1970 | 8,000 nm | PMOS | Intel | [316][304] |
| ? | ? | 1971 | 10,000 nm | PMOS | Intel | [408] |
| ? | 480 | 1971 | ? | PMOS | General Instrument | [318] |
| ? | ? | 1973 | ? | NMOS | Texas Instruments | [318] |
| ? | 220 | 1973 | ? | NMOS | Mostek | [318] |
| ? | ? | 1973 | 7,500 nm | NMOS | NEC | [18][17] |
| ? | ? | 1973 | 6,000 nm | PMOS | Toshiba | [19][409] |
| ? | ? | 1976 | 5,000 nm | NMOS | Hitachi, Intel | [318] |
| ? | ? | 1976 | 5,000 nm | CMOS | RCA | |
| ? | ? | 1976 | 4,000 nm | NMOS | Zilog | |
| ? | ? | 1976 | 3,000 nm | NMOS | Intel | [410] |
| ? | 1,850 | 1977 | ? | NMOS | NTT | [318] |
| ? | ? | 1978 | 3,000 nm | CMOS | Hitachi | [411] |
| ? | ? | 1978 | 2,500 nm | NMOS | Texas Instruments | [318] |
| ? | ? | 1978 | 2,000 nm | NMOS | NEC, NTT | |
| ? | 2,600 | 1979 | ? | VMOS | Siemens | |
| ? | 7,280 | 1979 | 1,000 nm | NMOS | NTT | |
| ? | 7,620 | 1980 | 1,000 nm | NMOS | NTT | |
| ? | ? | 1983 | 2,000 nm | CMOS | Toshiba | [322] |
| ? | ? | 1983 | 1,500 nm | CMOS | Intel | [318] |
| ? | ? | 1983 | 1,200 nm | CMOS | Intel | |
| ? | ? | 1984 | 800 nm | CMOS | NTT | |
| ? | ? | 1987 | 700 nm | CMOS | Fujitsu | |
| ? | ? | 1989 | 600 nm | CMOS | Mitsubishi, NEC, Toshiba | [322] |
| ? | ? | 1989 | 500 nm | CMOS | Hitachi, Mitsubishi, NEC, Toshiba | |
| ? | ? | 1991 | 400 nm | CMOS | Matsushita, Mitsubishi, Fujitsu, Toshiba | |
| ? | ? | 1993 | 350 nm | CMOS | Sony | |
| ? | ? | 1993 | 250 nm | CMOS | Hitachi, NEC | |
| 3LM | 32,000 | 1994 | 350 nm | CMOS | NEC | [206] |
| ? | ? | 1995 | 160 nm | CMOS | Hitachi | [322] |
| ? | ? | 1996 | 150 nm | CMOS | Mitsubishi | |
| TSMC 180 nm | ? | 1998 | 180 nm | CMOS | TSMC | [412] |
| CS80 | ? | 1999 | 180 nm | CMOS | Fujitsu | [413] |
| ? | ? | 1999 | 180 nm | CMOS | Intel, Sony, Toshiba | [312][218] |
| CS85 | ? | 1999 | 170 nm | CMOS | Fujitsu | [414] |
| Samsung 140 nm | ? | 1999 | 140 nm | CMOS | Samsung | [322] |
| ? | ? | 2001 | 130 nm | CMOS | Fujitsu, Intel | [413][312] |
| Samsung 100 nm | ? | 2001 | 100 nm | CMOS | Samsung | [322] |
| ? | ? | 2002 | 90 nm | CMOS | Sony, Toshiba, Samsung | [218][340] |
| CS100 | ? | 2003 | 90 nm | CMOS | Fujitsu | [413] |
| Intel 90 nm | 1,450,000 | 2004 | 90 nm | CMOS | Intel | [415][312] |
| Samsung 80 nm | ? | 2004 | 80 nm | CMOS | Samsung | [416] |
| ? | ? | 2004 | 65 nm | CMOS | Fujitsu, Toshiba | [417] |
| Samsung 60 nm | ? | 2004 | 60 nm | CMOS | Samsung | [340] |
| TSMC 45 nm | ? | 2004 | 45 nm | CMOS | TSMC | |
| Elpida 90 nm | ? | 2005 | 90 nm | CMOS | Elpida Memory | [418] |
| CS200 | ? | 2005 | 65 nm | CMOS | Fujitsu | [419][413] |
| Samsung 50 nm | ? | 2005 | 50 nm | CMOS | Samsung | [342] |
| Intel 65 nm | 2,080,000 | 2006 | 65 nm | CMOS | Intel | [415] |
| Samsung 40 nm | ? | 2006 | 40 nm | CMOS | Samsung | [342] |
| Toshiba 56 nm | ? | 2007 | 56 nm | CMOS | Toshiba | [343] |
| Matsushita 45 nm | ? | 2007 | 45 nm | CMOS | Matsushita | [81] |
| Intel 45 nm | 3,300,000 | 2008 | 45 nm | CMOS | Intel | [420] |
| Toshiba 43 nm | ? | 2008 | 43 nm | CMOS | Toshiba | [344] |
| TSMC 40 nm | ? | 2008 | 40 nm | CMOS | TSMC | [421] |
| Toshiba 32 nm | ? | 2009 | 32 nm | CMOS | Toshiba | [422] |
| Intel 32 nm | 7,500,000 | 2010 | 32 nm | CMOS | Intel | [420] |
| ? | ? | 2010 | 20 nm | CMOS | Hynix, Samsung | [423][342] |
| Intel 22 nm | 15,300,000 | 2012 | 22 nm | CMOS | Intel | [420] |
| IMFT 20 nm | ? | 2012 | 20 nm | CMOS | IMFT | [424] |
| Toshiba 19 nm | ? | 2012 | 19 nm | CMOS | Toshiba | |
| Hynix 16 nm | ? | 2013 | 16 nm | FinFET | SK Hynix | [423] |
| TSMC 16 nm | 28,880,000 | 2013 | 16 nm | FinFET | TSMC | [425][426] |
| Samsung 10 nm | 51,820,000 | 2013 | 10 nm | FinFET | Samsung | [427][428] |
| Intel 14 nm | 37,500,000 | 2014 | 14 nm | FinFET | Intel | [420] |
| 14LP | 32,940,000 | 2015 | 14 nm | FinFET | Samsung | [427] |
| TSMC 10 nm | 52,510,000 | 2016 | 10 nm | FinFET | TSMC | [425][429] |
| 12LP | 36,710,000 | 2017 | 12 nm | FinFET | GlobalFoundries, Samsung | [239] |
| N7FF | 96,500,000
101,850,000[430] |
2017 | 7 nm | FinFET | TSMC | [431][432][433] |
| 8LPP | 61,180,000 | 2018 | 8 nm | FinFET | Samsung | [427] |
| 7LPE | 95,300,000 | 2018 | 7 nm | FinFET | Samsung | [432] |
| Intel 10 nm | 100,760,000
106,100,000[430] |
2018 | 10 nm | FinFET | Intel | [434] |
| 5LPE | 126,530,000 | 2018 | 5 nm | FinFET | Samsung | [436][437] |
| N7FF+ | 113,900,000 | 2019 | 7 nm | FinFET | TSMC | [431][432] |
| CLN5FF | 171,300,000
185,460,000[430] |
2019 | 5 nm | FinFET | TSMC | [402] |
| Intel 7 | 100,760,000
106,100,000[430] |
2021 | 7 nm | FinFET | Intel | |
| 4LPE | 145,700,000[435] | 2021 | 4 nm | FinFET | Samsung | [438][439][440] |
| N4 | 196,600,000[430][441] | 2021 | 4 nm | FinFET | TSMC | [442] |
| N4P | 196,600,000[430][441] | 2022 | 4 nm | FinFET | TSMC | [443] |
| 3GAE | 202,850,000[430] | 2022 | 3 nm | MBCFET | Samsung | [444][438][445] |
| N3 | 314,730,000[430] | 2022 | 3 nm | FinFET | TSMC | [446][447] |
| N4X | ? | 2023 | 4 nm | FinFET | TSMC | [448][449][450] |
| N3E | ? | 2023 | 3 nm | FinFET | TSMC | [447][451] |
| 3GAP | ? | 2023 | 3 nm | MBCFET | Samsung | [438] |
| Intel 4 | 160,000,000[452] | 2023 | 4 nm | FinFET | Intel | [453][454][455] |
| Intel 3 | ? | 2023 | 3 nm | FinFET | Intel | [454][455] |
| Intel 20A | ? | 2024 | 2 nm | RibbonFET | Intel | [454][455] |
| Intel 18A | ? | 2025 | sub-2 nm | RibbonFET | Intel | [454] |
| 2GAP | ? | 2025 | 2 nm | MBCFET | Samsung | [438] |
| N2 | ? | 2025 | 2 nm | GAAFET | TSMC | [447][451] |
| Samsung 1.4 nm | ? | 2027 | 1.4 nm | ? | Samsung | [456] |
Gate count
[edit]In certain applications, the term gate count is preferred over the term transistor count. It refers to the number of logic gates built with transistors and other electronic devices needed to implement a design.[457][458][459][460]
See also
[edit]Notes
[edit]- ^ Microprocessor specialised for processing machine learning workloads, pioneered by UK based semiconductor startup Graphcore.
- ^ Declassified 1998
- ^ The TMS1000 is a microcontroller, the transistor count includes memory and input/output controllers, not just the CPU.
- ^ 2668 without depletion mode pull-up transistors
- ^ 3,510 without depletion mode pull-up transistors
- ^ 6,813 without depletion mode pull-up transistors
- ^ 3,900,000,000 core chiplet die, 2,090,000,000 I/O die
- ^ a b Estimate
- ^ Versal Premium are confirmed to be shipping in 1H 2021 but nothing was mentioned about the VP1802 in particular. Usually Xilinx makes separate news for the release of its biggest devices so the VP1802 is likely to be released later.
- ^ "Intelligence Processing Unit"
References
[edit]- ^ a b Hruska, Joel (August 2019). "Cerebras Systems Unveils 1.2 Trillion Transistor Wafer-Scale Processor for AI". extremetech.com. Retrieved September 6, 2019.
- ^ a b Feldman, Michael (August 2019). "Machine Learning chip breaks new ground with waferscale integration". nextplatform.com. Retrieved September 6, 2019.
- ^ a b Cutress, Ian (August 2019). "Hot Chips 31 Live Blogs: Cerebras' 1.2 Trillion Transistor Deep Learning Processor". anandtech.com. Archived from the original on August 20, 2019. Retrieved September 6, 2019.
- ^ a b "A Look at Cerebras Wafer-Scale Engine: Half Square Foot Silicon Chip". WikiChip Fuse. November 16, 2019. Retrieved December 2, 2019.
- ^ a b Everett, Joseph (August 26, 2020). "World's largest CPU has 850,000 7 nm cores that are optimized for AI and 2.6 trillion transistors". TechReportArticles.
- ^ "John Gustafson's answer to How many individual transistors are in the world's most powerful supercomputer?". Quora. Retrieved August 22, 2019.
- ^ Pires, Francisco (October 5, 2022). "Water-Based Chips Could be Breakthrough for Neural Networking, AI: Wetware has gained an entirely new meaning". Tom's Hardware. Retrieved October 5, 2022.
- ^ Laws, David (April 2, 2018). "13 Sextillion & Counting: The Long & Winding Road to the Most Frequently Manufactured Human Artifact in History". Computer History Museum.
- ^ Handy, Jim (May 26, 2014). "How Many Transistors Have Ever Shipped?". Forbes.
- ^ "1971: Microprocessor Integrates CPU Function onto a Single Chip". The Silicon Engine. Computer History Museum. Retrieved September 4, 2019.
- ^ a b Holt, Ray. "World's First Microprocessor". Retrieved March 5, 2016.
1st fully integrated chip set microprocessor
- ^ a b "Alpha 21364 - Microarchitectures - Compaq - WikiChip". en.wikichip.org. Retrieved September 8, 2019.
- ^ Holt, Ray M. (1998). The F14A Central Air Data Computer and the LSI Technology State-of-the-Art in 1968. p. 8.
- ^ Holt, Ray M. (2013). "F14 TomCat MOS-LSI Chip Set". First Microprocessor. Archived from the original on November 6, 2020. Retrieved November 6, 2020.
- ^ Ken Shirriff. "The Texas Instruments TMX 1795: the (almost) first, forgotten microprocessor". 2015.
- ^ Ryoichi Mori; Hiroaki Tajima; Morihiko Tajima; Yoshikuni Okada (October 1977). "Microprocessors in Japan". Euromicro Newsletter. 3 (4): 50–7. doi:10.1016/0303-1268(77)90111-0.
- ^ a b "NEC 751 (uCOM-4)". The Antique Chip Collector's Page. Archived from the original on May 25, 2011. Retrieved June 11, 2010.
- ^ a b "1970s: Development and evolution of microprocessors" (PDF). Semiconductor History Museum of Japan. Archived from the original (PDF) on June 27, 2019. Retrieved June 27, 2019.
- ^ a b "1973: 12-bit engine-control microprocessor (Toshiba)" (PDF). Semiconductor History Museum of Japan. Archived from the original (PDF) on June 27, 2019. Retrieved June 27, 2019.
- ^ "Low Bandwidth Timeline – Semiconductor". Texas Instruments. Retrieved June 22, 2016.
- ^ Ken Shirriff. "Inside the HP Nanoprocessor: a high-speed processor that can't even add". 2020. quote: "By my count, the Nanoprocessor has 4639 transistors. ... 3829 unique transistors. Of these, 1061 act as pull-ups, while 2668 are active. In comparison, the 6502 has 4237 transistors, of which 3218 are active. The 8008 has 3500 transistors and the Motorola 6800 has 4100 transistors."
- ^ "The MOS 6502 and the Best Layout Guy in the World". research.swtch.com. January 3, 2011. Retrieved September 3, 2019.
- ^ Shirriff, Ken (January 2023). "Counting the transistors in the 8086 processor: it's harder than you might think".
- ^ "Digital History: ZILOG Z8000 (APRIL 1979)". OLD-COMPUTERS.COM : The Museum. Retrieved June 19, 2019.
- ^ "Chip Hall of Fame: Motorola MC68000 Microprocessor". IEEE Spectrum. Institute of Electrical and Electronics Engineers. June 30, 2017. Retrieved June 19, 2019.
- ^ Microprocessors: 1971 to 1976 Archived December 3, 2013, at the Wayback Machine Christiansen
- ^ "Microprocessors 1976 to 1981". weber.edu. Archived from the original on December 3, 2013. Retrieved August 9, 2014.
- ^ "W65C816S 16-bit Core". www.westerndesigncenter.com. Retrieved September 12, 2017.
- ^ a b c d e Demone, Paul (November 9, 2000). "ARM's Race to World Domination". real world technologies. Retrieved July 20, 2015.
- ^ Hand, Tom. "The Harris RTX 2000 Microcontroller" (PDF). mpeforth.com. Retrieved August 9, 2014.
- ^ "Forth chips list". UltraTechnology. March 15, 2001. Retrieved August 9, 2014.
- ^ Koopman, Philip J. (1989). "4.4 Architecture of the Novix NC4016". Stack Computers: the new wave. Ellis Horwood Series in Computers and Their Applications. Carnegie Mellon University. ISBN 978-0745804187. Retrieved August 9, 2014.
- ^ "Fujitsu SPARC". cpu-collection.de. Retrieved June 30, 2019.
- ^ a b Kimura S, Komoto Y, Yano Y (1988). "Implementation of the V60/V70 and its FRM function". IEEE Micro. 8 (2): 22–36. doi:10.1109/40.527. S2CID 9507994.
- ^ "VL2333 - VTI - WikiChip". en.wikichip.org. Retrieved August 31, 2019.
- ^ Inayoshi H, Kawasaki I, Nishimukai T, Sakamura K (1988). "Realization of Gmicro/200". IEEE Micro. 8 (2): 12–21. doi:10.1109/40.526. S2CID 36938046.
- ^ Bosshart, P.; Hewes, C.; Mi-Chang Chang; Kwok-Kit Chau; Hoac, C.; Houston, T.; Kalyan, V.; Lusky, S.; Mahant-Shetti, S.; Matzke, D.; Ruparel, K.; Ching-Hao Shaw; Sridhar, T.; Stark, D. (October 1987). "A 553K-Transistor LISP Processor Chip". IEEE Journal of Solid-State Circuits. 22 (5): 202–3. doi:10.1109/ISSCC.1987.1157084. S2CID 195841103.
- ^ Fahlén, Lennart E.; Stockholm International Peace Research Institute (1987). "3. Hardware requirements for artificial intelligence § Lisp Machines: TI Explorer". Arms and Artificial Intelligence: Weapon and Arms Control Applications of Advanced Computing. SIPRI Monograph Series. Oxford University Press. p. 57. ISBN 978-0-19-829122-0.
- ^ Jouppi, Norman P.; Tang, Jeffrey Y. F. (July 1989). "A 20-MIPS Sustained 32-bit CMOS Microprocessor with High Ratio of Sustained to Peak Performance". IEEE Journal of Solid-State Circuits. 24 (5): i. Bibcode:1989IJSSC..24.1348J. CiteSeerX 10.1.1.85.988. doi:10.1109/JSSC.1989.572612. WRL Research Report 89/11.
- ^ "The CPU shack museum". CPUshack.com. May 15, 2005. Retrieved August 9, 2014.
- ^ a b c "Intel i960 Embedded Microprocessor". National High Magnetic Field Laboratory. Florida State University. March 3, 2003. Archived from the original on March 3, 2003. Retrieved June 29, 2019.
- ^ Venkatasawmy, Rama (2013). The Digitization of Cinematic Visual Effects: Hollywood's Coming of Age. Rowman & Littlefield. p. 198. ISBN 9780739176214.
- ^ Bakoglu, Grohoski, and Montoye. "The IBM RISC System/6000 processor: Hardware overview." IBM J. Research and Development. Vol. 34 No. 1, January 1990, pp. 12-22.
- ^ "SH Microprocessor Leading the Nomadic Era" (PDF). Semiconductor History Museum of Japan. Archived from the original (PDF) on June 27, 2019. Retrieved June 27, 2019.
- ^ "SH2: A Low Power RISC Micro for Consumer Applications" (PDF). Hitachi. Archived from the original (PDF) on May 10, 2019. Retrieved June 27, 2019.
- ^ "HARP-1: A 120 MHz Superscalar PA-RISC Processor" (PDF). Hitachi. Archived from the original (PDF) on April 23, 2016. Retrieved June 19, 2019.
- ^ White and Dhawan. "POWER2: next generation of the RISC System/6000 family" IBM J. Research and Development. Vol. 38 No. 5, September 1994, pp. 493-502.
- ^ "ARM7 Statistics". Poppyfields.net. May 27, 1994. Retrieved August 9, 2014.
- ^ "Forth Multiprocessor Chip MuP21". www.ultratechnology.com. Retrieved September 6, 2019.
MuP21 has a 21-bit CPU core, a memory coprocessor, and a video coprocessor
- ^ a b "F21 CPU". www.ultratechnology.com. Retrieved September 6, 2019.
F21 offers video I/O, analog I/O, serial network I/O, and a parallel I/O port on chip. F21 has a transistor count of about 15,000 vs about 7,000 for MuP21.
- ^ "Ars Technica: PowerPC on Apple: An Architectural History, Part I - Page 2 - (8/2004)". archive.arstechnica.com. Retrieved August 11, 2020.
- ^ Gary et al. (1994). "The PowerPC 603 microprocessor: a low-power design for portable applications." Proceedings of COMPCON 94. DOI: 10.1109/CMPCON.1994.282894
- ^ Slaton et al. (1995). "The PowerPC 603e microprocessor: an enhanced, low-power, superscalar microprocessor." Proceedings of ICCD '95 International Conference on Computer Design. DOI: 10.1109/ICCD.1995.528810
- ^ Bowhill, William J. et al. (1995). "Circuit Implementation of a 300-MHz 64-bit Second-generation CMOS Alpha CPU". Digital Technical Journal, Volume 7, Number 1, pp. 100–118.
- ^ "Intel Pentium Pro 180". hw-museum.cz. February 20, 2015. Retrieved September 8, 2019.
- ^ Kozierok, Charles M. (April 17, 2001). "PC Guide Intel Pentium Pro ("P6")". The PC Guide. Archived from the original on April 14, 2001. Retrieved August 9, 2014.
- ^ Gaddis, N.; Lotz, J. (November 1996). "A 64-b quad-issue CMOS RISC microprocessor". IEEE Journal of Solid-State Circuits 31 (11): pp. 1697–1702.
- ^ Bouchard, Gregg. "Design objectives of the 0.35 μm Alpha 21164 Microprocessor". IEEE Hot Chips Symposium, August 1996, IEEE Computer Society.
- ^ Ulf Samuelsson. "Transistor count of common uCs?". www.embeddedrelated.com. Retrieved September 8, 2019.
IIRC, The AVR core is 12,000 gates, and the megaAVR core is 20,000 gates. Each gate is 4 transistors. The chip is considerably larger since the memory uses quite a lot.
- ^ Gronowski, Paul E. et al. (May 1998). "High-performance microprocessor design". IEEE Journal of Solid-State Circuits 33 (5): pp. 676–686.
- ^ Nakagawa, Norio; Arakawa, Fumio (April 1999). "Entertainment Systems and High-Performance Processor SH-4" (PDF). Hitachi Review. 48 (2): 58–63. Retrieved March 18, 2023.
- ^ Nishii, O.; Arakawa, F.; Ishibashi, K.; Nakano, S.; Shimura, T.; Suzuki, K.; Tachibana, M.; Totsuka, Y.; Tsunoda, T.; Uchiyama, K.; Yamada, T.; Hattori, T.; Maejima, H.; Nakagawa, N.; Narita, S.; Seki, M.; Shimazaki, Y.; Satomura, R.; Takasuga, T.; Hasegawa, A. (1998). "A 200 MHZ 1.2 W 1.4 GFLOPS microprocessor with graphic operation unit". 1998 IEEE International Solid-State Circuits Conference. Digest of Technical Papers, ISSCC. First Edition (Cat. No. 98CH36156). IEEE. pp. 18.1-1 - 18.1-11. doi:10.1109/ISSCC.1998.672469. ISBN 0-7803-4344-1. S2CID 45392734.
- ^ a b c Diefendorff, Keith (April 19, 1999). "Sony's Emotionally Charged Chip: Killer Floating-Point "Emotion Engine" To Power PlayStation 2000" (PDF). Microprocessor Report. 13 (5). S2CID 29649747. Archived from the original (PDF) on February 28, 2019. Retrieved June 19, 2019.
- ^ a b Hennessy, John L.; Patterson, David A. (May 29, 2002). Computer Architecture: A Quantitative Approach (3 ed.). Morgan Kaufmann. p. 491. ISBN 978-0-08-050252-6. Retrieved April 9, 2013.
- ^ a b c "NVIDIA GeForce 7800 GTX GPU Review". PC Perspective. June 22, 2005. Retrieved June 18, 2019.
- ^ Ando, H.; Yoshida, Y.; Inoue, A.; Sugiyama, I.; Asakawa, T.; Morita, K.; Muta, T.; Otokurumada, T.; Okada, S.; Yamashita, H.; Satsukawa, Y.; Konmoto, A.; Yamashita, R.; Sugiyama, H. (2003). "A 1.3GHz fifth generation SPARC64 microprocessor". Proceedings of the 40th Annual Design Automation Conference. Design Automation Conference. pp. 702–705. doi:10.1145/775832.776010. ISBN 1-58113-688-9.
- ^ Krewell, Kevin (21 October 2002). "Fujitsu's SPARC64 V Is Real Deal". Microprocessor Report.
- ^ "Intel Pentium M Processor 1.60 GHZ, 1M Cache, 400 MHZ FSB Product Specifications".
- ^ "EE+GS". PS2 Dev Wiki.
- ^ "Sony MARKETING (JAPAN) ANNOUNCES LAUNCH OF "PSX" DESR-5000 and DESR-7000 TOWARDS THE END OF 2003" (Press release). Sony. November 27, 2003.
- ^ "EMOTION ENGINE AND GRAPHICS SYNTHESIZER USED IN THE CORE OF PLAYSTATION BECOME ONE CHIP" (PDF). Sony. April 21, 2003. Retrieved March 19, 2023.
- ^ "Sony PSX's 90nm CPU is 'not 90nm'". The Register. January 30, 2004.
- ^ "Semi Insights stands by 'not 90-nm' description of PSX chip". EE Times. February 5, 2004.
- ^ "Intel Pentium M Processor 760 (2M Cache, 2.00A GHZ, 533 MHZ FSB) Product Specifications".
- ^ Fujitsu Limited (August 2004). SPARC64 V Processor For UNIX Server.
- ^ "A Glimpse Inside The Cell Processor". Gamasutra. July 13, 2006. Retrieved June 19, 2019.
- ^ "Intel Pentium D Processor 920". Intel. Retrieved January 5, 2023.
- ^ "PRESS KIT — Dual-core Intel Itanium Processor". Intel. Retrieved August 9, 2014.
- ^ a b Toepelt, Bert (January 8, 2009). "AMD Phenom II X4: 45nm Benchmarked — The Phenom II And AMD's Dragon Platform". TomsHardware.com. Retrieved August 9, 2014.
- ^ "ARM (Advanced RISC Machines) Processors". EngineersGarage.com. Retrieved August 9, 2014.
- ^ a b "Panasonic starts to sell a New-generation UniPhier System LSI". Panasonic. October 10, 2007. Retrieved July 2, 2019.
- ^ "SPARC64 VI Extensions" page 56, Fujitsu Limited, Release 1.3, 27 March 2007
- ^ Morgan, Timothy Prickett (17 July 2008). "Fujitsu and Sun Flex Their Quads with New Sparc Server Lineup". The Unix Guardian, Vol. 8, No. 27.
- ^ Takumi Maruyama (2009). SPARC64 VIIIfx: Fujitsu's New Generation Octo Core Processor for PETA Scale computing (PDF). Proceedings of Hot Chips 21. IEEE Computer Society. Archived from the original (PDF) on October 8, 2010. Retrieved June 30, 2019.
- ^ "Intel Atom N450 specifications". Intel. Retrieved June 8, 2023.
- ^ "Intel Atom D510 specifications". Intel. Retrieved June 8, 2023.
- ^ Stokes, Jon (February 10, 2010). "Sun's 1 billion-transistor, 16-core Niagara 3 processor". ArsTechnica.com. Retrieved August 9, 2014.
- ^ "IBM to Ship World's Fastest Microprocessor". IBM. September 1, 2010. Archived from the original on September 5, 2010. Retrieved August 9, 2014.
- ^ "Intel to deliver first computer chip with two billion transistors". AFP. February 5, 2008. Archived from the original on May 20, 2011. Retrieved February 5, 2008.
- ^ "Intel Previews Intel Xeon 'Nehalem-EX' Processor." May 26, 2009. Retrieved on May 28, 2009.
- ^ Morgan, Timothy Prickett (November 21, 2011), "Fujitsu parades 16-core Sparc64 super stunner", The Register, retrieved December 8, 2011
- ^ Angelini, Chris (November 14, 2011). "Intel Core i7-3960X Review: Sandy Bridge-E And X79 Express". TomsHardware.com. Retrieved August 9, 2014.
- ^ "IDF2012 Mark Bohr, Intel Senior Fellow" (PDF).
- ^ "Images of SPARC64" (PDF). fujitsu.com. Retrieved August 29, 2017.
- ^ "Intel's Atom Architecture: The Journey Begins". AnandTech. Archived from the original on January 22, 2009. Retrieved April 4, 2010.
- ^ "Intel Xeon Phi SE10X". TechPowerUp. Retrieved July 20, 2015.
- ^ Shimpi, Lal. "The Haswell Review: Intel Core i7-4770K & i5-4670K Tested". anandtech. Archived from the original on June 7, 2013. Retrieved November 20, 2014.
- ^ "Dimmick, Frank (August 29, 2014). "Intel Core i7 5960X Extreme Edition Review". Overclockers Club. Retrieved August 29, 2014.
- ^ "Apple A8X". NotebookCheck. Retrieved July 20, 2015.
- ^ "Intel Readying 15-core Xeon E7 v2". AnandTech. Archived from the original on February 12, 2014. Retrieved August 9, 2014.
- ^ "Intel Xeon E5-2600 v3 Processor Overview: Haswell-EP Up to 18 Cores". pcper. September 8, 2014. Retrieved January 29, 2015.
- ^ "Intel's Broadwell-U arrives aboard 15W, 28W mobile processors". TechReport. January 5, 2015. Retrieved January 5, 2015.
- ^ "Oracle Cranks up the Cores to 32 with Sparc M7 Chip". EnterpriseTech. August 13, 2014.
- ^ "Broadwell-E: Intel Core i7-6950X, 6900K, 6850K & 6800K Review". Tom's Hardware. May 30, 2016. Retrieved April 12, 2017.
- ^ "The Broadwell-E Review". PC Gamer. July 8, 2016. Retrieved April 12, 2017.
- ^ "HUAWEI TO UNVEIL KIRIN 970 SOC WITH AI UNIT, 5.5 BILLION TRANSISTORS AND 1.2 GBPS LTE SPEED AT IFA 2017". firstpost.com. September 1, 2017. Retrieved November 18, 2018.
- ^ "Broadwell-EP Architecture - Intel Xeon E5-2600 v4 Broadwell-EP Review". Tom's Hardware. March 31, 2016. Retrieved April 4, 2016.
- ^ "About the ZipCPU". zipcpu.com. Retrieved September 10, 2019.
As of ORCONF, 2016, the ZipCPU used between 1286 and 4926 6-LUTs, depending upon how it is configured.
- ^ "Qualcomm Snapdragon 835 (8998)". NotebookCheck. Retrieved September 23, 2017.
- ^ Takahashi, Dean (January 3, 2017). "Qualcomm's Snapdragon 835 will debut with 3 billion transistors and a 10nm manufacturing process". VentureBeat.
- ^ Singh, Teja (2017). "3.2 Zen: A Next-Generation High-Performance x86 Core". Proc. IEEE International Solid-State Circuits Conference. pp. 52–54.
- ^ Cutress, Ian (February 22, 2017). "AMD Launches Zen". Anandtech.com. Archived from the original on February 22, 2017. Retrieved February 22, 2017.
- ^ "Ryzen 5 1600 - AMD". Wikichip.org. April 20, 2018. Retrieved December 9, 2018.
- ^ "Kirin 970 – HiSilicon". Wikichip. March 1, 2018. Retrieved November 8, 2018.
- ^ a b Leadbetter, Richard (April 6, 2017). "Inside the next Xbox: Project Scorpio tech revealed". Eurogamer. Retrieved May 3, 2017.
- ^ "Intel Xeon Platinum 8180". TechPowerUp. December 1, 2018. Retrieved December 2, 2018.
- ^ Pellerano, Stefano (March 2, 2022). "Circuit Design to Harness the Power of Scaling and Integration (ISSCC 2022)". YouTube.
- ^ Lee, Y. "SiFive Freedom SoCs : Industry's First Open Source RISC V Chips" (PDF). HotChips 29 IOT/Embedded. Archived from the original (PDF) on August 9, 2020. Retrieved June 19, 2019.
- ^ "Documents at Fujitsu" (PDF). fujitsu.com. Retrieved August 29, 2017.
- ^ Schmerer, Kai (November 5, 2018). "iPad Pro 2018: A12X-Prozessor bietet deutlich mehr Leistung". ZDNet.de (in German).
- ^ "Qualcomm Datacenter Technologies Announces Commercial Shipment of Qualcomm Centriq 2400 – The World's First 10nm Server Processor and Highest Performance Arm-based Server Processor Family Ever Designed". Qualcomm. Retrieved November 9, 2017.
- ^ "Qualcomm Snapdragon 1000 for laptops could pack 8.5 billion transistors". techradar. Retrieved September 23, 2017.
- ^ "Spotted: Qualcomm Snapdragon 8cx Wafer on 7nm". AnandTech. Archived from the original on December 7, 2018. Retrieved December 6, 2018.
- ^ "HiSilicon Kirin 710". Notebookcheck. September 19, 2018. Retrieved November 24, 2018.
- ^ Yang, Daniel; Wegner, Stacy (September 21, 2018). "Apple iPhone Xs Max Teardown". TechInsights. Retrieved September 21, 2018.
- ^ "Apple's A12 Bionic is the first 7-nanometer smartphone chip". Engadget. Retrieved September 26, 2018.
- ^ "Kirin 980 – HiSilicon". Wikichip. November 8, 2018. Retrieved November 8, 2018.
- ^ "Qualcomm Snapdragon 8180: 7nm SoC SDM1000 With 8.5 Billion Transistors To Challenge Apple A12 Bionic Chipset". dailyhunt. Retrieved September 21, 2018.
- ^ Zafar, Ramish (October 30, 2018). "Apple's A12X Has 10 Billion Transistors, 90% Performance Boost & 7-Core GPU". Wccftech.
- ^ "Fujitsu began to produce Japan's billions of super-calculations with the strongest ARM processor A64FX". firstxw.com. April 16, 2019. Archived from the original on June 20, 2019. Retrieved June 19, 2019.
- ^ "Fujitsu Successfully Triples the Power Output of Gallium-Nitride Transistors". Fujitsu. August 22, 2018. Retrieved June 19, 2019.
- ^ "Hot Chips 30: Nvidia Xavier SoC". fuse.wikichip.org. September 18, 2018. Retrieved December 6, 2018.
- ^ Frumusanu, Andrei. "The Samsung Galaxy S10+ Snapdragon & Exynos Review: Almost Perfect, Yet So Flawed". www.anandtech.com. Archived from the original on March 29, 2019. Retrieved February 19, 2021.
- ^ a b c d e f "Zen 2 Microarchitecture". WikiChip. Retrieved February 21, 2023.
- ^ "AMD Ryzen 9 3900X and Ryzen 7 3700X Review: Zen 2 and 7nm Unleashed". Tom's Hardware. July 7, 2019. Retrieved October 19, 2019.
- ^ Frumusanu, Andrei. "The Huawei Mate 30 Pro Review: Top Hardware without Google?". AnandTech. Archived from the original on January 2, 2020. Retrieved January 2, 2020.
- ^ Zafar, Ramish (September 10, 2019). "Apple A13 For iPhone 11 Has 8.5 Billion Transistors, Quad-Core GPU". Wccftech. Retrieved September 11, 2019.
- ^ Introducing iPhone 11 Pro — Apple Youtube Video, retrieved September 11, 2019[dead YouTube link]
- ^ "Hot Chips 2020 Live Blog: IBM z15". AnandTech. August 17, 2020. Archived from the original on August 17, 2020.
- ^ a b Broekhuijsen, Niels (October 23, 2019). "AMD's 64-Core EPYC and Ryzen CPUs Stripped: A Detailed Inside Look". Retrieved October 24, 2019.
- ^ a b Mujtaba, Hassan (October 22, 2019). "AMD 2nd Gen EPYC Rome Processors Feature A Gargantuan 39.54 Billion Transistors, IO Die Pictured in Detail". Retrieved October 24, 2019.
- ^ Friedman, Alan (December 14, 2019). "5nm Kirin 1020 SoC tipped for next year's Huawei Mate 40 line". Phone Arena. Retrieved December 23, 2019.
- ^ Verheyde, Arne (December 5, 2019). "Amazon Compares 64-core ARM Graviton2 to Intel's Xeon". Tom's Hardware. Retrieved December 6, 2019.
- ^ Morgan, Timothy Prickett (December 3, 2019). "Finally: AWS Gives Servers A Real Shot In The Arm". The Next Platform. Retrieved December 6, 2019.
- ^ Friedman, Alan (October 10, 2019). "Qualcomm will reportedly introduce the Snapdragon 865 SoC as soon as next month". Phone Arena. Retrieved February 19, 2021.
- ^ "Xiaomi Mi 10 Teardown Analysis | TechInsights". www.techinsights.com. Retrieved February 19, 2021.
- ^ "The Linley Group - TI Jacinto Accelerates Level 3 ADAS". www.linleygroup.com. Retrieved February 12, 2021.
- ^ "Apple unveils A14 Bionic processor with 40% faster CPU and 11.8 billion transistors". Venturebeat. November 10, 2020. Retrieved November 24, 2020.
- ^ "Apple says new Arm-based M1 chip offers the 'longest battery life ever in a Mac'". The Verge. November 10, 2020. Retrieved November 11, 2020.
- ^ Ikoba, Jed John (October 23, 2020). "Multiple benchmark tests rank the Kirin 9000 as one of the most-powerful chipset yet". Gizmochina. Retrieved November 14, 2020.
- ^ Frumusanu, Andrei. "Huawei Announces Mate 40 Series: Powered by 15.3bn Transistors 5nm Kirin 9000". www.anandtech.com. Archived from the original on October 22, 2020. Retrieved November 14, 2020.
- ^ a b Burd, Thomas (2022). "2.7 Zen3: The AMD 2nd-Generation 7nm x86-64 Microprocessor Core". Proc. IEEE International Solid-State Circuits Conference. pp. 54–56.
- ^ "For a long time, Intel once again named the number of transistors in the chip. There are supposed to be about 6 billion for Rocket Lake-S. Coffee Lake-S is supposed to have about 4 billion. The chip with eight cores is about 30 % bigger than the predecessor with ten core". twitter. Retrieved March 16, 2021.
- ^ "Intel's Core i7-11700K 'Rocket Lake' Delidded: A Big Die, Revealed". tomshardware. March 12, 2021. Retrieved March 16, 2021.
- ^ "Intel's 14nm density". www.techcenturion.com. November 26, 2019. Retrieved November 26, 2019.
- ^ "AMD Ryzen 7 5800H Specs". TechPowerUp. Retrieved September 20, 2021.
- ^ "AMD Epyc 7763 specifications". August 2023.
- ^ Shankland, Stephen. "Apple's A15 Bionic chip powers iPhone 13 with 15 billion transistors, new graphics and AI". CNET. Retrieved September 20, 2021.
- ^ "Apple iPhone 13 Pro Teardown | TechInsights". www.techinsights.com. Retrieved September 29, 2021.
- ^ a b "Apple unveils M1 Pro and M1 Max chips for latest MacBook Pro laptops". VentureBeat. October 18, 2021.
- ^ "Apple Announces M1 Pro & M1 Max: Giant New Arm SoCs with All-Out Performance". AnanadTech. Archived from the original on October 18, 2021. Retrieved December 2, 2021.
- ^ "Apple unveils new computer chips amid shortage". BBC News. October 19, 2021.
- ^ a b "Apple Joins 3D-Fabric Portfolio with M1 Ultra?". TechInsights. Retrieved July 8, 2022.
- ^ "Hot Chips 2020 live blog". AnandTech. August 17, 2020. Archived from the original on August 17, 2020.
- ^ "Phantom X2 Series 5G powered by MediaTek Dimensity 9000". Mediatek. December 12, 2022.
- ^ "MediaTek Dimensity 9000". Mediatek. January 21, 2023.
- ^ "Apple A16 Bionic announced for the iPhone 14 Pro and iPhone 14 Pro Max". NotebookCheck. September 7, 2022.
- ^ "iPhone 14 Pro and Pro Max Only Models to Get New A16 Chip". CNET. September 7, 2022.
- ^ "The Apple 2022 Fall iPhone Event Live Blog". AnandTech. September 7, 2022. Archived from the original on September 8, 2022.
- ^ "Apple unveils M1 Ultra, the world's most powerful chip for a personal computer". Apple Newsroom. Retrieved March 9, 2022.
- ^ Shankland, Stephen. "Meet Apple's Enormous 20-Core M1 Ultra Processor, the Brains in the New Mac Studio Machine". CNET. Retrieved March 9, 2022.
- ^ a b "AMD releases Milan-X CPUs". AnandTech. March 21, 2022. Archived from the original on March 29, 2022.
- ^ "IBM Telum Hot Chips slide deck" (PDF). August 23, 2021.
- ^ "IBM z16 announcement". April 5, 2022.
- ^ "Apple unveils M2, taking the breakthrough performance and capabilities of M1 even further". Apple. June 6, 2022.
- ^ "MediaTek Dimensity 9200: New flagship chipset debuts with ARM Cortex-X3 CPU and Immortalis-G715 GPU cores built around TSMC N4P node". NotebookCheck. November 8, 2022.
- ^ "Dimensity 9200 specs". Mediatek. November 8, 2022.
- ^ "Dimensity 9200 presentation". Mediatek. November 8, 2022.
- ^ "AMD EPYC Genoa Gaps Intel Xeon in Stunning Fashion". ServeTheHome. November 10, 2022.
- ^ "AMD Aims to Break the ZettaFLOP Barrier by 2035, Lays Down Next-Gen Plans to Resolve Efficiency Problems". Appuals. February 21, 2023.
- ^ "AMD Lays The Path To Zettascale Computing: Talks CPU & GPU Performance Plus Efficiency Trends, Next-Gen Chiplet Packaging & More". WCCFtech. February 20, 2023.
- ^ "AMD EPYC Genoa & SP5 Platform Leaked – 5nm Zen 4 CCD Measures Roughly 72mm, 12 CCD Package at 5428mm2, Up To 700W Peak Socket Power". WCCFtech. August 17, 2021.
- ^ Syed, Areej (August 17, 2021). "Leaked AMD Epyc Genoa Docs Reveal 96 Cores, Max TDP of 700W, and Zen 4 Chiplet Dimensions". HardwareTimes.
- ^ "Kirin 9000S has about 6 billion fewer transistors than Kirin 9000, but its performance is stronger! How did you do it?". iNews. September 13, 2023. Retrieved September 24, 2023.
- ^ "Apple Announces M4 SoC: Latest and Greatest Starts on 2024 iPad Pro". Anandtech. May 7, 2024. Archived from the original on May 7, 2024.
- ^ a b c "Apple introduces new M3 chip lineup, starting with the M3, M3 Pro, and M3 Max". Arstechnica. October 31, 2023.
- ^ Goldman, Joshua. "Apple A17 Pro Chip: The New Brain Inside iPhone 15 Pro, Pro Max". CNET. Retrieved September 12, 2023.
- ^ "4th Gen Intel Xeon Scalable Sapphire Rapids Leaps Forward". ServeTheHome. January 10, 2023.
- ^ "Wie vier Dies zu einem "monolithischen" Sapphire Rapids werden". hardwareLUXX. February 21, 2022.
- ^ a b "Apple unveils M2 Pro and M2 Max: next-generation chips for next-level workflows". Apple (Press release). January 17, 2023.
- ^ "Apple introduces M2 Ultra" (Press release). Apple. June 5, 2023.
- ^ "AMD EPYC Bergamo Launched 128 Cores Per Socket and 1024 Threads Per 1U". ServeTheHome. June 13, 2023.
- ^ "AMD Instinct MI300A Accelerators". AMD. Retrieved January 14, 2024.
- ^ Alcorn, Paul (December 6, 2023). "AMD unveils Instinct MI300X GPU and MI300A APU, claims up to 1.6X lead over Nvidia's competing GPUs". Tom's Hardware. Retrieved January 14, 2024.
- ^ "China creates world's thinnest chip with 5931 transistors". 2025.
- ^ Williams, Chris. "Nvidia's Tesla P100 has 15 billion transistors, 21TFLOPS". www.theregister.co.uk. Retrieved August 12, 2019.
- ^ "Famous Graphics Chips: NEC μPD7220 Graphics Display Controller". IEEE Computer Society. Institute of Electrical and Electronics Engineers. August 22, 2018. Retrieved June 21, 2019.
- ^ "GPU History: Hitachi ARTC HD63484. The second graphics processor". IEEE Computer Society. Institute of Electrical and Electronics Engineers. October 7, 2018. Retrieved June 21, 2019.
- ^ "Big Book of Amiga Hardware".
- ^ Russell, Jesse; Cohn, Ronald (May 2012). MOS Technology Agnus. Book on Demand. ISBN 978-5511916842.
- ^ a b "30 Years of Console Gaming". Klinger Photography. August 20, 2017. Retrieved June 19, 2019.
- ^ "Sega Saturn". MAME. Retrieved July 18, 2019.
- ^ "ASIC CHIPS ARE INDUSTRY'S GAME WINNERS". The Washington Post. September 18, 1995. Retrieved June 19, 2019.
- ^ "Is it Time to Rename the GPU?". Jon Peddie Research. IEEE Computer Society. July 9, 2018. Retrieved June 19, 2019.
- ^ "FastForward Sony Taps LSI Logic for PlayStation Video Game CPU Chip". FastForward. Retrieved January 29, 2014.
- ^ a b "Reality Co-Processor − The Power In Nintendo64" (PDF). Silicon Graphics. August 26, 1997. Archived from the original (PDF) on May 19, 2020. Retrieved June 18, 2019.
- ^ "Imagination PowerVR PCX2 GPU". VideoCardz.net. Retrieved June 19, 2019.
- ^ a b c d e f g h Lilly, Paul (May 19, 2009). "From Voodoo to GeForce: The Awesome History of 3D Graphics". PC Gamer. Retrieved June 19, 2019.
- ^ a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al am "3D accelerator database". Vintage 3D. Retrieved July 21, 2019.
- ^ "RIVA128 Datasheet". SGS Thomson Microelectronics. Retrieved July 21, 2019.
- ^ a b c Singer, Graham (April 3, 2013). "History of the Modern Graphics Processor, Part 2". TechSpot. Retrieved July 21, 2019.
- ^ "Remembering the Sega Dreamcast". Bit-Tech. September 29, 2009. Retrieved June 18, 2019.
- ^ Weinberg, Neil (September 7, 1998). "Comeback kid". Forbes. Retrieved June 19, 2019.
- ^ Charles, Bertie (1998). "Sega's New Dimension". Forbes. 162 (5–9). Forbes Incorporated: 206.
The chip, etched in 0.25-micron detail — state-of-the-art for graphics processors — fits 10 million transistors
- ^ Hagiwara, Shiro; Oliver, Ian (November–December 1999). "Sega Dreamcast: Creating a Unified Entertainment World". IEEE Micro. 19 (6). IEEE Computer Society: 29–35. doi:10.1109/40.809375. Archived from the original on August 23, 2000. Retrieved June 27, 2019.
- ^ "VideoLogic Neon 250 4MB". VideoCardz.net. Retrieved June 19, 2019.
- ^ Shimpi, Anand Lal (November 21, 1998). "Fall Comdex '98 Coverage". AnandTech. Archived from the original on March 10, 2011. Retrieved June 19, 2019.
- ^ a b c "EMOTION ENGINE AND GRAPHICS SYNTHESIZER USED IN THE CORE OF PLAYSTATION BECOME ONE CHIP" (PDF). Sony. April 21, 2003. Retrieved June 26, 2019.
- ^ "NVIDIA NV10 A3 GPU Specs". TechPowerUp. Retrieved June 19, 2019.
- ^ IGN Staff (November 4, 2000). "Gamecube Versus PlayStation 2". IGN. Retrieved November 22, 2015.
- ^ "NVIDIA NV2A GPU Specs". TechPowerUp. Retrieved July 21, 2019.
- ^ "ATI Xenos GPU Specs". TechPowerUp. Retrieved June 21, 2019.
- ^ International, GamesIndustry (July 14, 2005). "TSMC to manufacture X360 GPU". Eurogamer. Retrieved August 22, 2006.
- ^ "NVIDIA Playstation 3 RSX 65nm Specs". TechPowerUp. Retrieved June 21, 2019.
- ^ "PS3 Graphics Chip Goes 65nm in Fall". Edge Online. June 26, 2008. Archived from the original on July 25, 2008.
- ^ "NVIDIA's 1.4 Billion Transistor GPU: GT200 Arrives as the GeForce GTX 280 & 260". AnandTech.com. Archived from the original on June 17, 2008. Retrieved August 9, 2014.
- ^ "The Radeon HD 4850 & 4870: AMD Wins at $199 and $299". AnandTech.com. Archived from the original on May 30, 2012. Retrieved August 9, 2014.
- ^ a b Glaskowsky, Peter. "ATI and Nvidia face off-obliquely". CNET. Archived from the original on January 27, 2012. Retrieved August 9, 2014.
- ^ Woligroski, Don (December 22, 2011). "AMD Radeon HD 7970". TomsHardware.com. Retrieved August 9, 2014.
- ^ "NVIDIA Kepler GK110 Architecture" (PDF). NVIDIA. 2012. Retrieved January 9, 2024.
- ^ Smith, Ryan (November 12, 2012). "NVIDIA Launches Tesla K20 & K20X: GK110 Arrives At Last". AnandTech. Archived from the original on November 13, 2012.
- ^ "Whitepaper: NVIDIA GeForce GTX 680" (PDF). NVIDIA. 2012. Archived from the original (PDF) on April 17, 2012.
- ^ a b Kan, Michael (August 18, 2020). "Xbox Series X May Give Your Wallet a Workout Due to High Chip Manufacturing Costs". PCMag. Retrieved September 5, 2020.
- ^ "AMD Xbox One GPU". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "AMD PlayStation 4 GPU". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "AMD Xbox One S GPU". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "AMD PlayStation 4 Pro GPU". www.techpowerup.com. Retrieved February 5, 2020.
- ^ Smith, Ryan (June 29, 2016). "The AMD RX 480 Preview". Anandtech.com. Archived from the original on June 30, 2016. Retrieved February 22, 2017.
- ^ a b c Schor, David (July 22, 2018). "VLSI 2018: GlobalFoundries 12nm Leading-Performance, 12LP". WikiChip Fuse. Retrieved May 31, 2019.
- ^ Harris, Mark (April 5, 2016). "Inside Pascal: NVIDIA's Newest Computing Platform". Nvidia developer blog.
- ^ a b c d e f "GPU Database: Pascal". TechPowerUp. July 26, 2023.
- ^ "AMD Xbox One X GPU". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "Radeon's next-generation Vega architecture" (PDF).
- ^ Durant, Luke; Giroux, Olivier; Harris, Mark; Stam, Nick (May 10, 2017). "Inside Volta: The World's Most Advanced Data Center GPU". Nvidia developer blog.
- ^ "NVIDIA TURING GPU ARCHITECTURE: Graphics Reinvented" (PDF). Nvidia. 2018. Retrieved June 28, 2019.
- ^ "NVIDIA GeForce GTX 1650". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "NVIDIA GeForce GTX 1660 Ti". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "AMD Radeon RX 5700 XT". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "AMD Radeon RX 5500 XT". www.techpowerup.com. Retrieved February 5, 2020.
- ^ "AMD Arcturus GPU Specs". TechPowerUp. Retrieved November 10, 2022.
- ^ Walton, Jared (May 14, 2020). "Nvidia Unveils Its Next-Generation 7nm Ampere A100 GPU for Data Centers, and It's Absolutely Massive". Tom's Hardware.
- ^ "Nvidia Ampere Architecture". www.nvidia.com. Retrieved May 15, 2020.
- ^ "NVIDIA GA102 GPU Specs". Techpowerup. Retrieved September 5, 2020.
- ^ "'Giant Step into the Future': NVIDIA CEO Unveils GeForce RTX 30 Series GPUs". www.nvidia.com. September 2020. Retrieved September 5, 2020.
- ^ "NVIDIA GA103 GPU Specs". TechPowerUp. Retrieved March 21, 2023.
- ^ "NVIDIA GeForce RTX 3070 Specs". TechPowerUp. Retrieved September 20, 2021.
- ^ "NVIDIA GA106 specs". TechPowerUp. Retrieved March 22, 2023.
- ^ "NVIDIA GA107 GPU Specs". TechPowerUp. Retrieved March 21, 2023.
- ^ "MI250X die size estimates". Twitter. November 17, 2021.
- ^ "AMD Instinct MI250 Professional Graphics Card". VideoCardz. November 2, 2022.
- ^ "AMD's Instinct MI250X OAM Card Pictured: Aldebaran's Massive Die Revealed". Tom's Hardware. November 17, 2021.
- ^ "AMD MI250X and Toplogies Explained at HC34". ServeTheHome. August 22, 2022.
- ^ "Nvidia Launches Hopper H100 GPU, New DGXs and Grace Superchips". HPCWire. March 22, 2022. Retrieved March 23, 2022.
- ^ "NVIDIA details AD102 GPU, up to 18432 CUDA cores, 76.3B transistors and 608 mm2". VideoCardz. September 20, 2022.
- ^ a b "NVIDIA confirms Ada 102/103/104 GPU specs, AD104 has more transistors than GA102". VideoCardz. September 23, 2022.
- ^ a b "Alleged Nvidia AD106 and AD107 GPU Pics, Specs, Die Sizes Revealed". Tom's Hardware. February 3, 2023.
- ^ "NVIDIA GeForce RTX 4060 Ti "AD106-350" GPU Pictured, Uses Samsung GDDR6 Dies". WCCFtech. April 28, 2023.
- ^ "NVIDIA's Smallest Ada GPU, The AD107-400, For GeForce RTX 4060 GPUs Pictured". WCCFtech. May 21, 2023.
- ^ "AMD Unveils World's Most Advanced Gaming Graphics Cards, Built on Groundbreaking AMD RDNA 3 Architecture with Chiplet Design". AMD (Press release). November 3, 2022.
- ^ "AMD Announces the $999 Radeon RX 7900 XTX... (endnote RX-819)". TechPowerUp. November 4, 2022.
- ^ "AMD Navi 31 GPU Specs". TechPowerUp. Retrieved November 7, 2023.
- ^ "AMD Navi 32 GPU Specs". TechPowerUp. Retrieved November 7, 2023.
- ^ "AMD Navi 33 GPU Specs". TechPowerUp. Retrieved March 21, 2023.
- ^ "AMD Has a GPU to Rival Nvidia's H100". HPCWire. June 13, 2023. Retrieved June 14, 2023.
- ^ "AMD Aqua Vanjaram Specs". TechPowerUp. Retrieved January 14, 2024.
- ^ "NVIDIA Blackwell Platform Arrives to Power a New Era of Computing" (Press release). March 18, 2024.
- ^ "NVIDIA GB202 GPU Specs". TechPowerUp. Retrieved October 27, 2025.
- ^ "NVIDIA GB203 GPU Specs". TechPowerUp. Retrieved October 27, 2025.
- ^ "NVIDIA GB205 GPU Specs". TechPowerUp. Retrieved October 27, 2025.
- ^ "NVIDIA GB206 GPU Specs". TechPowerUp. Retrieved October 27, 2025.
- ^ "NVIDIA GB207 GPU Specs". TechPowerUp. Retrieved October 27, 2025.
- ^ "AMD Navi 44 GPU Specs". TechPowerUp. Retrieved October 27, 2025.
- ^ "AMD Navi 48 GPU Specs". TechPowerUp. Retrieved October 27, 2025.
- ^ "Taiwan Company UMC Delivers 65nm FPGAs to Xilinx." SDA-ASIA Thursday, November 9, 2006.
- ^ ""Altera's new 40nm FPGAs — 2.5 billion transistors!". pldesignline.com. Archived from the original on June 19, 2010. Retrieved January 22, 2009.
- ^ "Design of a High-Density SoC FPGA at 20nm" (PDF). 2014. Archived from the original (PDF) on April 23, 2016. Retrieved July 16, 2017.
- ^ Maxfield, Clive (October 2011). "New Xilinx Virtex-7 2000T FPGA provides equivalent of 20 million ASIC gates". EETimes. AspenCore. Retrieved September 4, 2019.
- ^ Greenhill, D.; Ho, R.; Lewis, D.; Schmit, H.; Chan, K. H.; Tong, A.; Atsatt, S.; How, D.; McElheny, P. (February 2017). "3.3 a 14nm 1GHz FPGA with 2.5D transceiver integration". 2017 IEEE International Solid-State Circuits Conference (ISSCC). pp. 54–55. doi:10.1109/ISSCC.2017.7870257. ISBN 978-1-5090-3758-2. S2CID 2135354.
- ^ Greenhill, David; Ho, Ron; Lewis, David; Schmit, Herman; Chan, Kok Hong; Tong, Andy; Atsatt, Sean; How, Dana; McElheny, Peter; Duwel, Keith; Schulz, Jeffrey; Faulkner, Darren; Iyer, Gopal; Chen, George; Phoon, Hee Kong; Lim, Han Wooi; Koay, Wei-Yee; Garibay, Ty (May 17, 2017). "3.3 A 14nm 1GHz FPGA with 2.5D transceiver integration | DeepDyve". DeepDyve. Archived from the original on May 17, 2017. Retrieved September 19, 2019.
- ^ Santarini, Mike (May 2014). "Xilinx Ships Industry's First 20-nm All Programmable Devices" (PDF). Xcell journal. No. 86. Xilinx. p. 14. Retrieved June 3, 2014.
- ^ Gianelli, Silvia (January 2015). "Xilinx Delivers the Industry's First 4M Logic Cell Device, Offering >50M Equivalent ASIC Gates and 4X More Capacity than Competitive Alternatives". www.xilinx.com. Retrieved August 22, 2019.
- ^ Sims, Tara (August 2019). "Xilinx Announces the World's Largest FPGA Featuring 9 Million System Logic Cells". www.xilinx.com. Retrieved August 22, 2019.
- ^ Verheyde, Arne (August 2019). "Xilinx Introduces World's Largest FPGA With 35 Billion Transistors". www.tomshardware.com. Retrieved August 23, 2019.
- ^ Cutress, Ian (August 2019). "Xilinx Announces World Largest FPGA: Virtex Ultrascale+ VU19P with 9m Cells". www.anandtech.com. Archived from the original on August 27, 2019. Retrieved September 25, 2019.
- ^ Abazovic, Fuad (May 2019). "Xilinx 7nm Versal taped out last year". Retrieved September 30, 2019.
- ^ Cutress, Ian (August 2019). "Hot Chips 31 Live Blogs: Xilinx Versal AI Engine". Archived from the original on August 20, 2019. Retrieved September 30, 2019.
- ^ Krewell, Kevin (August 2019). "Hot Chips 2019 highlights new AI strategies". Retrieved September 30, 2019.
- ^ Leibson, Steven (November 6, 2019). "Intel announces Intel Stratix 10 GX 10M FPGA, worlds highest capacity with 10.2 million logic elements". Retrieved November 7, 2019.
- ^ Verheyde, Arne (November 6, 2019). "Intel Introduces World's Largest FPGA With 43.3 Billion Transistors". Retrieved November 7, 2019.
- ^ Cutress, Ian (August 2020). "Hot Chips 2020 Live Blog: Xilinx Versal ACAPs". Archived from the original on August 18, 2020. Retrieved September 9, 2020.
- ^ "Xilinx Announces Full Production Shipments of 7nm Versal AI Core and Versal Prime Series Devices". April 27, 2021. Retrieved May 8, 2021.
- ^ a b The DRAM memory of Robert Dennard history-computer.com
- ^ a b c d "Late 1960s: Beginnings of MOS memory" (PDF). Semiconductor History Museum of Japan. January 23, 2019. Retrieved June 27, 2019.
- ^ a b c d e f "1970: Semiconductors compete with magnetic cores". Computer History Museum. Retrieved June 19, 2019.
- ^ "2.1.1 Flash Memory". TU Wien. Retrieved June 20, 2019.
- ^ Shilov, Anton. "SK Hynix Starts Production of 128-Layer 4D NAND, 176-Layer Being Developed". www.anandtech.com. Archived from the original on June 26, 2019. Retrieved September 16, 2019.
- ^ "Samsung Begins Production of 100+ Layer Sixth-Generation V-NAND Flash". PC Perspective. August 11, 2019. Retrieved September 16, 2019.
- ^ a b "1966: Semiconductor RAMs Serve High-speed Storage Needs". Computer History Museum. Retrieved June 19, 2019.
- ^ "Specifications for Toshiba "TOSCAL" BC-1411". Old Calculator Web Museum. Archived from the original on July 3, 2017. Retrieved May 8, 2018.
- ^ "Toshiba "Toscal" BC-1411 Desktop Calculator". Old Calculator Web Museum. Archived from the original on May 20, 2007.
- ^ Castrucci, Paul (May 10, 1966). "IBM first in IC memory" (PDF). IBM News. Vol. 3, no. 9. IBM Corporation. Retrieved June 19, 2019 – via Computer History Museum.
- ^ a b c d e f g h i j k l m "A chronological list of Intel products. The products are sorted by date" (PDF). Intel museum. Intel Corporation. July 2005. Archived from the original (PDF) on August 9, 2007. Retrieved July 31, 2007.
- ^ a b "1970s: SRAM evolution" (PDF). Semiconductor History Museum of Japan. Retrieved June 27, 2019.
- ^ a b c Pimbley, J. (2012). Advanced CMOS Process Technology. Elsevier. p. 7. ISBN 9780323156806.
- ^ a b "Intel: 35 Years of Innovation (1968–2003)" (PDF). Intel. 2003. Archived from the original (PDF) on November 4, 2021. Retrieved June 26, 2019.
- ^ a b Lojek, Bo (2007). History of Semiconductor Engineering. Springer Science & Business Media. pp. 362–363. ISBN 9783540342588.
The i1103 was manufactured on a 6-mask silicon-gate P-MOS process with 8 μm minimum features. The resulting product had a 2,400 μm2 memory cell size, a die size just under 10 mm2, and sold for around $21.
- ^ "Manufacturers in Japan enter the DRAM market and integration densities are improved" (PDF). Semiconductor History Museum of Japan. Retrieved June 27, 2019.
- ^ a b c d e f g h i j k l m n Gealow, Jeffrey Carl (August 10, 1990). "Impact of Processing Technology on DRAM Sense Amplifier Design" (PDF). Massachusetts Institute of Technology. pp. 149–166. Retrieved June 25, 2019 – via CORE.
- ^ "Silicon Gate MOS 2102A". Intel. Retrieved June 27, 2019.
- ^ "One of the Most Successful 16K Dynamic RAMs: The 4116". National Museum of American History. Smithsonian Institution. Retrieved June 20, 2019.
- ^ Component Data Catalog (PDF). Intel. 1978. pp. 3–94. Retrieved June 27, 2019.
- ^ a b c d e f g h i j k l m n o p q r s t "Memory". STOL (Semiconductor Technology Online). Archived from the original on November 2, 2023. Retrieved June 25, 2019.
- ^ "The Cutting Edge of IC Technology: The First 294,912-Bit (288K) Dynamic RAM". National Museum of American History. Smithsonian Institution. Retrieved June 20, 2019.
- ^ "Computer History for 1984". Computer Hope. Retrieved June 25, 2019.
- ^ "Japanese Technical Abstracts". Japanese Technical Abstracts. 2 (3–4). University Microfilms: 161. 1987.
The announcement of 1M DRAM in 1984 began the era of megabytes.
- ^ "KM48SL2000-7 Datasheet". Samsung. August 1992. Retrieved June 19, 2019.
- ^ "Electronic Design". Electronic Design. 41 (15–21). Hayden Publishing Company. 1993.
The first commercial synchronous DRAM, the Samsung 16-Mbit KM48SL2000, employs a single-bank architecture that lets system designers easily transition from asynchronous to synchronous systems.
- ^ Breaking the gigabit barrier, DRAMs at ISSCC portend major system-design impact. (dynamic random access memory; International Solid-State Circuits Conference; Hitachi Ltd. and NEC Corp. research and development), January 9, 1995
- ^ a b "Japanese Company Profiles" (PDF). Smithsonian Institution. 1996. Retrieved June 27, 2019.
- ^ a b "History: 1990s". SK Hynix. Archived from the original on February 5, 2021. Retrieved July 6, 2019.
- ^ "Samsung 50nm 2GB DDR3 chips are industry's smallest". SlashGear. September 29, 2008. Retrieved June 25, 2019.
- ^ Shilov, Anton (July 19, 2017). "Samsung Increases Production Volumes of 8 GB HBM2 Chips Due to Growing Demand". AnandTech. Archived from the original on July 20, 2017. Retrieved June 29, 2019.
- ^ "Samsung Unleashes a Roomy DDR4 256GB RAM". Tom's Hardware. September 6, 2018. Archived from the original on June 21, 2019. Retrieved June 21, 2019.
- ^ "First 3D Nanotube and RRAM ICs Come Out of Foundry". IEEE Spectrum: Technology, Engineering, and Science News. July 19, 2019. Retrieved September 16, 2019.
This wafer was made just last Friday... and it's the first monolithic 3D IC ever fabricated within a foundry
- ^ "Three Dimensional Monolithic System-on-a-Chip". www.darpa.mil. Retrieved September 16, 2019.
- ^ "DARPA 3DSoC Initiative Completes First Year, Update Provided at ERI Summit on Key Steps Achieved to Transfer Technology into SkyWater's 200mm U.S. Foundry". Skywater Technology Foundry (Press release). July 25, 2019. Retrieved September 16, 2019.
- ^ "DD28F032SA Datasheet". Intel. Retrieved June 27, 2019.
- ^ "TOSHIBA ANNOUNCES 0.13 MICRON 1Gb MONOLITHIC NAND FEATURING LARGE BLOCK SIZE FOR IMPROVED WRITE/ERASE SPEED PERFORMANCE". Toshiba. September 9, 2002. Archived from the original on March 11, 2006. Retrieved March 11, 2006.
- ^ "TOSHIBA AND SANDISK INTRODUCE A ONE GIGABIT NAND FLASH MEMORY CHIP, DOUBLING CAPACITY OF FUTURE FLASH PRODUCTS". Toshiba. November 12, 2001. Retrieved June 20, 2019.
- ^ a b c d "Our Proud Heritage from 2000 to 2009". Samsung Semiconductor. Samsung. Retrieved June 25, 2019.
- ^ "TOSHIBA ANNOUNCES 1 GIGABYTE COMPACTFLASH CARD". Toshiba. September 9, 2002. Archived from the original on March 11, 2006. Retrieved March 11, 2006.
- ^ a b c d "History". Samsung Electronics. Samsung. Retrieved June 19, 2019.
- ^ a b "TOSHIBA COMMERCIALIZES INDUSTRY'S HIGHEST CAPACITY EMBEDDED NAND FLASH MEMORY FOR MOBILE CONSUMER PRODUCTS". Toshiba. April 17, 2007. Archived from the original on November 23, 2010. Retrieved November 23, 2010.
- ^ a b "Toshiba Launches the Largest Density Embedded NAND Flash Memory Devices". Toshiba. August 7, 2008. Retrieved June 21, 2019.
- ^ "Toshiba Launches Industry's Largest Embedded NAND Flash Memory Modules". Toshiba. June 17, 2010. Retrieved June 21, 2019.
- ^ "Samsung e·MMC Product family" (PDF). Samsung Electronics. December 2011. Archived from the original (PDF) on November 8, 2019. Retrieved July 15, 2019.
- ^ Shilov, Anton (December 5, 2017). "Samsung Starts Production of 512 GB UFS NAND Flash Memory: 64-Layer V-NAND, 860 MB/s Reads". AnandTech. Archived from the original on December 5, 2017. Retrieved June 23, 2019.
- ^ Manners, David (January 30, 2019). "Samsung makes 1TB flash eUFS module". Electronics Weekly. Retrieved June 23, 2019.
- ^ Tallis, Billy (October 17, 2018). "Samsung Shares SSD Roadmap for QLC NAND And 96-layer 3D NAND". AnandTech. Archived from the original on October 18, 2018. Retrieved June 27, 2019.
- ^ "Micron's 232 Layer NAND Now Shipping". AnandTech. July 26, 2022. Archived from the original on July 27, 2022.
- ^ "232-Layer NAND". Micron. Retrieved October 17, 2022.
- ^ "First to Market, Second to None: the World's First 232-Layer NAND". Micron. July 26, 2022.
- ^ "Comparison: Latest 3D NAND Products from YMTC, Samsung, SK hynix and Micron". TechInsights. January 11, 2023.
- ^ Han-Way Huang (December 5, 2008). Embedded System Design with C805. Cengage Learning. p. 22. ISBN 978-1-111-81079-5. Archived from the original on April 27, 2018.
- ^ Marie-Aude Aufaure; Esteban Zimányi (January 17, 2013). Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures. Springer. p. 136. ISBN 978-3-642-36318-4. Archived from the original on April 27, 2018.
- ^ a b c d "1965: Semiconductor Read-Only-Memory Chips Appear". Computer History Museum. Retrieved June 20, 2019.
- ^ "1971: Reusable semiconductor ROM introduced". The Storage Engine. Computer History Museum. Retrieved June 19, 2019.
- ^ Iizuka, H.; Masuoka, F.; Sato, Tai; Ishikawa, M. (1976). "Electrically alterable avalanche-injection-type MOS READ-ONLY memory with stacked-gate structure". IEEE Transactions on Electron Devices. 23 (4): 379–387. Bibcode:1976ITED...23..379I. doi:10.1109/T-ED.1976.18415. ISSN 0018-9383. S2CID 30491074.
- ^ μCOM-43 SINGLE CHIP MICROCOMPUTER: USERS' MANUAL (PDF). NEC Microcomputers. January 1978. Retrieved June 27, 2019.
- ^ "2716: 16K (2K x 8) UV ERASABLE PROM" (PDF). Intel. Archived from the original (PDF) on September 13, 2020. Retrieved June 27, 2019.
- ^ "1982 CATALOG" (PDF). NEC Electronics. Retrieved June 20, 2019.
- ^ Component Data Catalog (PDF). Intel. 1978. pp. 1–3. Retrieved June 27, 2019.
- ^ "27256 Datasheet" (PDF). Intel. Retrieved July 2, 2019.
- ^ "History of Fujitsu's Semiconductor Business". Fujitsu. Retrieved July 2, 2019.
- ^ "D27512-30 Datasheet" (PDF). Intel. Retrieved July 2, 2019.
- ^ "A Computer Pioneer Rediscovered, 50 Years On". The New York Times. April 20, 1994. Archived from the original on November 4, 2016.
- ^ "History of Computers and Computing, Birth of the modern computer, Relays computer, George Stibitz". history-computer.com. Retrieved August 22, 2019.
Initially the 'Complex Number Computer' performed only complex multiplication and division, but later a simple modification enabled it to add and subtract as well. It used about 400-450 binary relays, 6-8 panels, and ten multiposition, multipole relays called "crossbars" for temporary storage of numbers.
- ^ a b c d e "1953: Transistorized Computers Emerge". Computer History Museum. Retrieved June 19, 2019.
- ^ a b "ETL Mark III Transistor-Based Computer". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ a b "Brief History". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ "1962: Aerospace systems are first the applications for ICs in computers | The Silicon Engine | Computer History Museum". www.computerhistory.org. Retrieved September 2, 2019.
- ^ a b "PDP-8 (Straight 8) Computer Functional Restoration". www.pdp8.net. Retrieved August 22, 2019.
backplanes contain 230 cards, approximately 10,148 diodes, 1409 transistors, 5615 resistors, and 1674 capacitors
- ^ "IBM 608 calculator". IBM. January 23, 2003. Retrieved March 8, 2021.
- ^ "【NEC】 NEAC-2201". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ "【Hitachi and Japanese National Railways】 MARS-1". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ The IBM 7070 Data Processing System. Avery et al. (page 167)
- ^ "【Matsushita Electric Industrial】 MADIC-I transistor-based computer". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ "【NEC】 NEAC-2203". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ "【Toshiba】 TOSBAC-2100". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ 7090 Data Processing System
- ^ Luigi Logrippo. "My first two computers: Elea 9003 and Elea 6001: Memories of a 'bare-metal' programmer".
- ^ "【Mitsubishi Electric】 MELCOM 1101". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ Erich Bloch (1959). The Engineering Design of the Stretch Computer (PDF). Eastern Joint Computer Conference.
- ^ "【NEC】NEAC-L2". IPSJ Computer Museum. Information Processing Society of Japan. Retrieved June 19, 2019.
- ^ Thornton, James (1970). Design of a Computer: the Control Data 6600. p. 20.
- ^ "Digital Equipment PDP-8/S".
- ^ "The PDP-8/S - an exercise in cost reduction"
- ^ "PDP-8/S"
- ^ "The Digital Equipment Corporation PDP-8: Models and Options: The PDP-8/I".
- ^ James F. O'Loughlin. "PDP-8/I: bigger on the inside yet smaller on the outside".
- ^ Jan M. Rabaey, Digital Integrated Circuits, Fall 2001: Course Notes, Chapter 6: Designing Combinatorial Logic Gates in CMOS, retrieved October 27, 2012.
- ^ Richard F. Tinder (January 2000). Engineering Digital Design. Academic Press. ISBN 978-0-12-691295-1.
- ^ a b c d Engineers, Institute of Electrical Electronics (2000). 100-2000 (7th ed.). doi:10.1109/IEEESTD.2000.322230. ISBN 978-0-7381-2601-2. IEEE Std 100-2000.
- ^ a b c Smith, Kevin (August 11, 1983). "Image processor handles 256 pixels simultaneously". Electronics.
- ^ Kanellos, Michael (February 9, 2005). "Cell chip: Hit or hype?". CNET News. Archived from the original on October 25, 2012.
- ^ Kennedy, Patrick (June 2019). "Hands-on With a Graphcore C2 IPU PCIe Card at Dell Tech World". servethehome.com. Retrieved December 29, 2019.
- ^ "Colossus – Graphcore". en.wikichip.org. Retrieved December 29, 2019.
- ^ Graphcore. "IPU Technology". www.graphcore.ai.
- ^ "Cerebras Unveils 2nd Gen Wafer Scale Engine: 850,000 Cores, 2.6 Trillion Transistors - ExtremeTech". www.extremetech.com. April 21, 2021. Retrieved April 22, 2021.
- ^ "Cerebras Wafer Scale Engine WSE-2 and CS-2 at Hot Chips 34". ServeTheHome. August 23, 2022.
- ^ "NVIDIA NVLink4 NVSwitch at Hot Chips 34". ServeTheHome. August 22, 2022.
- ^ a b Schor, David (April 6, 2019). "TSMC Starts 5-Nanometer Risk Production". WikiChip Fuse. Retrieved April 7, 2019.
- ^ "1960: Metal Oxide Semiconductor (MOS) Transistor Demonstrated". Computer History Museum. Retrieved July 17, 2019.
- ^ Lojek, Bo (2007). History of Semiconductor Engineering. Springer Science & Business Media. pp. 321–3. ISBN 9783540342588.
- ^ "1963: Complementary MOS Circuit Configuration is Invented". Computer History Museum. Retrieved July 6, 2019.
- ^ "1964: First Commercial MOS IC Introduced". Computer History Museum. Retrieved July 17, 2019.
- ^ a b Lojek, Bo (2007). History of Semiconductor Engineering. Springer Science & Business Media. p. 330. ISBN 9783540342588.
- ^ Lambrechts, Wynand; Sinha, Saurabh; Abdallah, Jassem Ahmed; Prinsloo, Jaco (2018). Extending Moore's Law through Advanced Semiconductor Design and Processing Techniques. CRC Press. p. 59. ISBN 9781351248655.
- ^ Belzer, Jack; Holzman, Albert G.; Kent, Allen (1978). Encyclopedia of Computer Science and Technology: Volume 10 – Linear and Matrix Algebra to Microorganisms: Computer-Assisted Identification. CRC Press. p. 402. ISBN 9780824722609.
- ^ "Intel Microprocessor Quick Reference Guide". Intel. Retrieved June 27, 2019.
- ^ "1978: Double-well fast CMOS SRAM (Hitachi)" (PDF). Semiconductor History Museum of Japan. Retrieved July 5, 2019.
- ^ "0.18-micron Technology". TSMC. Retrieved June 30, 2019.
- ^ a b c d 65nm CMOS Process Technology
- ^ Diefendorff, Keith (15 November 1999). "Hal Makes Sparcs Fly". Microprocessor Report, Volume 13, Number 5.
- ^ a b Cutress, Ian. "Intel's 10nm Cannon Lake and Core i3-8121U Deep Dive Review". AnandTech. Archived from the original on January 30, 2019. Retrieved June 19, 2019.
- ^ "Samsung Shows Industry's First 2-Gigabit DDR2 SDRAM". Samsung Semiconductor. Samsung. September 20, 2004. Retrieved June 25, 2019.
- ^ Williams, Martyn (July 12, 2004). "Fujitsu, Toshiba begin 65nm chip trial production". InfoWorld. Retrieved June 26, 2019.
- ^ Elpida's presentation at Via Technology Forum 2005 and Elpida 2005 Annual Report
- ^ "Fujitsu Introduces World-class 65-Nanometer Process Technology for Advanced Server, Mobile Applications". Archived from the original on September 27, 2011. Retrieved June 20, 2019.
- ^ a b c d "Intel Now Packs 100 Million Transistors in Each Square Millimeter". IEEE Spectrum: Technology, Engineering, and Science News. March 30, 2017. Retrieved November 14, 2018.
- ^ "40nm Technology". TSMC. Retrieved June 30, 2019.
- ^ "Toshiba Makes Major Advances in NAND Flash Memory with 3-bit-per-cell 32nm generation and with 4-bit-per-cell 43nm technology". Toshiba. February 11, 2009. Retrieved June 21, 2019.
- ^ a b "History: 2010s". SK Hynix. Archived from the original on April 29, 2021. Retrieved July 8, 2019.
- ^ Shimpi, Anand Lal (June 8, 2012). "SandForce Demos 19nm Toshiba & 20nm IMFT NAND Flash". AnandTech. Archived from the original on June 9, 2012. Retrieved June 19, 2019.
- ^ a b Schor, David (April 16, 2019). "TSMC Announces 6-Nanometer Process". WikiChip Fuse. Retrieved May 31, 2019.
- ^ "16/12nm Technology". TSMC. Retrieved June 30, 2019.
- ^ a b c "VLSI 2018: Samsung's 8nm 8LPP, a 10nm extension". WikiChip Fuse. July 1, 2018. Retrieved May 31, 2019.
- ^ "Samsung Mass Producing 128Gb 3-bit MLC NAND Flash". Tom's Hardware. April 11, 2013. Archived from the original on June 21, 2019. Retrieved June 21, 2019.
- ^ "10nm Technology". TSMC. Retrieved June 30, 2019.
- ^ a b c d e f g h i "Can TSMC maintain their process technology lead". SemiWiki. April 29, 2020.
- ^ a b Jones, Scotten (May 3, 2019). "TSMC and Samsung 5nm Comparison". Semiwiki. Retrieved July 30, 2019.
- ^ a b c Nenni, Daniel (January 2, 2019). "Samsung vs TSMC 7nm Update". Semiwiki. Retrieved July 6, 2019.
- ^ "7nm Technology". TSMC. Retrieved June 30, 2019.
- ^ Schor, David (June 15, 2018). "A Look at Intel's 10nm Std Cell as TechInsights Reports on the i3-8121U, finds Ruthenium". WikiChip Fuse. Retrieved May 31, 2019.
- ^ a b "Samsung Foundry update 2019". SemiWiki. August 6, 2019.
- ^ Jones, Scotten (June 25, 2018), 7nm, 5nm and 3nm Logic, current and projected processes
- ^ Shilov, Anton. "Samsung Completes Development of 5nm EUV Process Technology". AnandTech. Archived from the original on April 18, 2019. Retrieved May 31, 2019.
- ^ a b c d "Samsung Foundry Innovations Power the Future of Big Data, AI/ML and Smart, Connected Devices". October 7, 2021.
- ^ "Qualcomm confirms Snapdragon 8 Gen 1 is made using Samsung's 4nm process". December 2, 2021.
- ^ Wilde, Damien (January 14, 2022). "List of Snapdragon 8 Gen 1 smartphones available since December 2021". 9to5Google.
- ^ a b "TSMC Extends Its 5nm Family With A New Enhanced-Performance N4P Node". WikiChip. October 26, 2021.
- ^ "MediaTek Launches Dimensity 9000 built on TSMC N4 process". December 16, 2021.
- ^ "TSMC Expands Advanced Technology Leadership with N4P Process (press release)". TSMC. October 26, 2021.
- ^ Armasu, Lucian (January 11, 2019), "Samsung Plans Mass Production of 3nm GAAFET Chips in 2021", www.tomshardware.com
- ^ "Samsung Starts 3nm Production: The Gate-All-Around (GAAFET) Era Begins". AnandTech. June 30, 2022. Archived from the original on July 2, 2022.
- ^ "TSMC Plans New Fab for 3nm". EE Times. December 12, 2016. Retrieved September 26, 2019.
- ^ a b c "TSMC Roadmap Update: 3nm in Q1 2023, 3nm Enhanced in 2024, 2nm in 2025". www.anandtech.com. October 18, 2021. Archived from the original on October 18, 2021.
- ^ "TSMC Introduces N4X Process (press release)". TSMC. December 16, 2021.
- ^ "The Future Is Now (blog post)". TSMC. December 16, 2021.
- ^ "TSMC Unveils N4X Node". AnandTech. December 17, 2021. Archived from the original on May 25, 2022.
- ^ a b "TSMC roadmap update". AnandTech. April 22, 2022. Archived from the original on April 25, 2022.
- ^ Smith, Ryan (June 13, 2022). "Intel 4 Process Node In Detail: 2x Density Scaling, 20% Improved Performance". AnandTech. Archived from the original on June 13, 2022.
- ^ Alcorn, Paul (March 24, 2021). "Intel Fixes 7nm, Meteor Lake and Granite Rapids Coming in 2023". Tom's Hardware. Retrieved June 1, 2021.
- ^ a b c d Cutress, Dr Ian. "Intel's Process Roadmap to 2025: with 4nm, 3nm, 20A and 18A?!". www.anandtech.com. Archived from the original on July 26, 2021. Retrieved July 27, 2021.
- ^ a b c Cutress, Dr Ian (February 17, 2022). "Intel Discloses Multi-Generation Xeon Scalable Roadmap: New E-Core Only Xeons in 2024". www.anandtech.com. Archived from the original on February 17, 2022.
- ^ "Samsung Electronics Unveils Plans for 1.4nm Process Technology and Investment for Production Capacity at Samsung Foundry Forum 2022". Samsung Global Newsroom. October 4, 2022.
- ^ Wecker, Dave; Bauer, Bela; Clark, Bryan K.; Hastings, Matthew B.; Troyer, Matthias (2014). "Gate-count estimates for performing quantum chemistry on small quantum computers". Physical Review A. 90 (2) 022305. arXiv:1312.1695. Bibcode:2014PhRvA..90b2305W. doi:10.1103/PhysRevA.90.022305.
- ^ Does gate count matter? Hardware efficiency of logic-minimization techniques for cryptographic primitives
- ^ Sarmento, Jose; Stonick, John T. (2010). "A minimal-gate-count fully digital frequency-tracking oversampling CDR circuit". Proceedings of 2010 IEEE International Symposium on Circuits and Systems. pp. 2099–2102. doi:10.1109/ISCAS.2010.5537061. ISBN 978-1-4244-5308-5.
- ^ Ghoniem, Omar; Elsayed, Hatem; Soubra, Hassan (2023). "Quantum Gate Count Analysis". 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS). pp. 190–197. doi:10.1109/ICICIS58388.2023.10391119. ISBN 979-8-3503-2208-8.
External links
[edit]Transistor count
View on GrokipediaFundamentals
Definition and Importance
The transistor count refers to the total number of transistors integrated into an electronic device, most commonly within integrated circuits (ICs), where it functions as the foremost metric for assessing circuit complexity and scale. Transistors operate primarily as electronic switches that control the flow of electrical current or as amplifiers that boost signal strength, forming the foundational building blocks for both digital logic and analog functions in modern electronics.[11][12] This count holds profound importance because it directly influences the potential for miniaturization, enhanced performance, and improved power efficiency in computing systems; greater numbers of transistors enable the realization of more intricate logic gates, interconnect networks, and storage elements, thereby expanding computational capabilities without proportionally increasing physical size or energy demands.[2][13] By allowing denser packing of functionality onto silicon chips, transistor count has been instrumental in driving the exponential advancement of electronics, from basic signal processing to complex artificial intelligence applications.[14] The notion of transistor count gained prominence in the evolution of ICs following the 1947 invention of the point-contact transistor by John Bardeen, Walter Brattain, and William Shockley at Bell Laboratories, which supplanted bulky vacuum tubes and laid the groundwork for compact, reliable circuitry.[15] Early ICs exemplified modest counts, such as the two-transistor flip-flop demonstrated by Jack Kilby at Texas Instruments in 1958, whereas today's system-on-chips routinely incorporate billions of transistors to support multifaceted operations.[16][17] This historical trajectory highlights transistor count's role as a catalyst for the sustained growth in electronic sophistication.[18]Measurement Methods
Transistor counts in integrated circuits (ICs) are determined through two primary methods: direct enumeration from design data and indirect estimation based on physical parameters. Direct counting relies on analyzing digital representations of the circuit during or after the design process. Electronic design automation (EDA) tools, such as those provided by Synopsys and Cadence, parse netlists—textual descriptions of circuit connectivity—and library cells to tally transistor instances, often during synthesis or place-and-route stages.[19][20] For post-layout verification, tools process GDSII files, the industry-standard binary format for IC mask layouts, to extract and count transistor-level elements by interpreting geometric shapes and layers that represent active devices.[21][22] This approach ensures precision but requires access to proprietary design files, which are typically unavailable outside the fabricating company. When direct access is limited, estimation provides a practical alternative by leveraging measurable attributes like die dimensions and process-specific densities. The fundamental formula for this is , where represents the estimated transistor count, is the transistor density (typically in millions of transistors per square millimeter, derived from standard cell benchmarks like NAND gates), and is the die area in square millimeters, often obtained from microscopy or manufacturer specifications.[2][23] Density values are calibrated using representative logic blocks, accounting for variations across the chip, though they may overestimate or underestimate due to non-uniform layouts. Process nodes influence these density figures by dictating minimum feature sizes that affect packing efficiency. Counting transistors presents several challenges, primarily related to what qualifies as a countable element and inconsistencies in reporting. Counts generally focus on active transistors—those functioning as switches or amplifiers—excluding passive structures like resistors or capacitors formed from transistor-like geometries, which can constitute 20-30% of total devices in complex layouts.[24] For memory arrays, such as ROMs, counts may include "potential" transistors (uncommitted sites) rather than physically implemented ones, further complicating comparisons.[25] Variations in standards arise from differing manufacturer practices; for instance, Intel historically disclosed detailed counts until 2014, after which estimates became necessary, while AMD's reports may emphasize different architectural inclusions, leading to non-standardized public figures often used for marketing rather than rigorous benchmarking.[26] These discrepancies highlight the need for contextual interpretation when comparing IC complexities.Density and Scaling
Moore's Law
Moore's Law originated from an observation made by Gordon E. Moore, co-founder of Intel, in his 1965 article "Cramming More Components onto Integrated Circuits," where he predicted that the number of components on an integrated circuit would double every year, driven by the need to maintain cost-effectiveness in semiconductor manufacturing.[18] This exponential growth was based on trends in early integrated circuit production, projecting that such scaling would allow for more complex and affordable electronics. In 1975, Moore revised his prediction in a presentation titled "Progress in Digital Integrated Electronics," adjusting the doubling period to every two years to better align with observed technological and economic realities.[27] The mechanism behind Moore's Law primarily involves the progressive shrinking of transistor dimensions through advancements in lithography and fabrication techniques, which enable higher densities while reducing power consumption and increasing performance per unit cost. This scaling directly correlates transistor count with improvements in computational capability, as more transistors facilitate enhanced logic functions and data processing efficiency. Historically, this trend is evidenced by the progression from the Intel 4004 microprocessor in 1971, which contained approximately 2,300 transistors, to modern processors exceeding tens of billions, demonstrating a roughly biennial doubling rate that closely matches Moore's revised forecast.[26] Quantitative analysis of Intel's chip data confirms doubling times of about 14 to 25 months over decades, underscoring the law's empirical validity.[26] Mathematically, Moore's Law can be expressed as , where is the transistor count at time , is the initial count, and years represents the doubling interval. However, since the 2010s, the pace of scaling has slowed due to physical constraints, including quantum tunneling effects that cause electron leakage in ultra-small transistors, challenging the continued exponential increase in density. As of 2025, Moore's Law remains a key benchmark for the semiconductor industry, though its traditional form is being extended through innovations like 3D transistor stacking, which allow for higher effective densities beyond planar scaling limits.[28] These approaches, including through-silicon vias and hybrid bonding, enable vertical integration to sustain performance gains despite atomic-scale barriers in two-dimensional fabrication.[29]Process Nodes
In semiconductor manufacturing, process nodes refer to generations of fabrication technology characterized by a nominal feature size, typically expressed in nanometers (nm), which historically represented the minimum dimension that could be patterned, such as gate length or half-pitch, but has evolved into a marketing designation not directly corresponding to literal physical measurements.[30] For instance, in advanced nodes like 7nm or 5nm, the actual gate length often exceeds the node name, with contacted poly pitch around 50-60nm and minimum metal pitch 30-40nm, decoupling the label from precise geometry to reflect overall scaling achievements.[31] This evolution traces back to the 1970s, when nodes began at 10μm for early integrated circuits, progressively shrinking through decades of optical lithography improvements to sub-2nm equivalents by the mid-2020s, enabling exponential transistor density growth while confronting quantum effects and manufacturing limits.[32] Key architectural innovations have driven this progression, including the adoption of FinFET (fin field-effect transistor) structures from the 14nm node through 3nm, where vertical fins enhance gate control over the channel to mitigate short-channel effects and boost drive current without excessive scaling of planar dimensions.[33] Transitioning to gate-all-around FET (GAAFET) or nanosheet designs at 2nm and beyond further encircles the channel with the gate, offering superior electrostatic control, reduced leakage, and tunable width for optimized performance-power tradeoffs compared to FinFETs.[34] Complementing these are extreme ultraviolet (EUV) lithography tools, introduced at 7nm and refined with high-NA optics for sub-2nm nodes, enabling patterning of features below 20nm half-pitch by using 13.5nm wavelengths to overcome diffraction limits of deep ultraviolet light. Transistor density, measured in millions of transistors per square millimeter (MTr/mm²), has scaled dramatically with node advancements, exemplifying the engineering feats behind density improvements; TSMC's 7nm node achieves approximately 91 MTr/mm² for logic, a roughly threefold increase over 16nm, facilitated by EUV for tighter pitches. Projections for 2nm nodes indicate further gains, with TSMC's N2 targeting around 200-237 MTr/mm², representing a 15% density uplift over its 3nm process through GAAFET stacking and optimized interconnects.[35] These metrics underscore conceptual shifts toward 3D transistor architectures and advanced patterning to sustain areal efficiency amid planar scaling slowdowns. As of 2025, leading foundries have advanced sub-2nm production: TSMC's N2 GAAFET-based node is scheduled to enter mass production in the second half of 2025, with volume production beginning before year-end as announced in October 2025, delivering 15% performance gains or 30% power reductions over 3nm at iso-speed, bolstered by backside power delivery (BPD) to minimize IR drop and enable denser routing.[36][37] Samsung's second-generation 2nm GAA process, featuring multi-bridge-channel FET (MBCFET) with tunable nanosheets, commenced volume manufacturing in Q4 2025, with yields around 50-60% and up to 8% efficiency improvements over 3nm, aiming for similar 15-20% density improvements while addressing yield challenges through refined gate stacks.[38][39] Intel's 18A (1.8nm equivalent) node, incorporating RibbonFET GAA and PowerVia BPD, achieved high-volume production readiness in 2025, offering up to 30% density gains and 15% better performance-per-watt over Intel 3, though the preceding 20A node was canceled in favor of external sourcing.[40] These nodes grapple with power leakage exacerbated by atomic-scale channels, where quantum tunneling increases subthreshold currents, prompting innovations like BPD to separate power rails from signal lines, reducing resistance by 20-30% and curbing dynamic power losses in high-density layouts.[41] GAAFET adoption, now widespread at 2nm, further suppresses leakage via full channel gating, with BPD integration projected to enhance overall node viability through 1.4nm scales.[42]Device Categories
Microprocessors
Microprocessors, central to general-purpose computing, have seen exponential growth in transistor counts since their inception, driven by the need for enhanced performance in desktops, laptops, servers, and mobile devices. The Intel 4004, introduced in 1971 as the first commercial microprocessor, contained approximately 2,300 transistors, enabling basic arithmetic and control functions on a 10-micrometer process. By the 1980s, designs like the Intel 80386 reached around 275,000 transistors, incorporating more complex instruction sets and pipelining for improved efficiency. This progression accelerated in the 1990s and 2000s with multi-core architectures; for instance, the Intel Core 2 Duo in 2006 featured about 291 million transistors per die, balancing clock speed increases with power efficiency. The shift toward system-on-chip (SoC) designs in the 2010s integrated additional components, further boosting counts, as seen in ARM-based processors for mobile computing which prioritized energy efficiency over raw x86 performance. Key factors influencing transistor counts in modern microprocessors include the number of cores, cache hierarchy size, and integration of peripherals such as GPUs, memory controllers, and I/O interfaces. Higher core counts, from dual-core in the early 2000s to 128-core server chips as of 2025, directly scale transistor usage for parallel processing in tasks like AI inference and virtualization. Large last-level caches, often exceeding 100 MB in high-end designs, consume significant transistors to reduce latency and boost throughput. ARM architectures, dominant in mobile SoCs, achieve comparable performance to x86 with fewer transistors per core due to simpler instruction decoding and lower overhead, enabling devices like smartphones to pack billions of transistors into compact, power-efficient packages. In contrast, x86 processors from Intel and AMD incorporate more complex out-of-order execution units, leading to higher counts but greater power draw. Chiplet-based designs, where multiple smaller dies are interconnected via high-bandwidth links like Infinity Fabric or EMIB, allow modular scaling; this approach mitigates yield issues on advanced nodes while combining specialized tiles for compute, I/O, and accelerators. Recent 3nm processes, such as those in Apple's silicon, further densify transistors, with a single core potentially rivaling older multi-core chips in capability. Illustrative examples highlight these trends through 2025. AMD's Zen 5 architecture, powering the Ryzen 9000 series desktop processors released in 2024, features up to 8.3 billion transistors in an 8-core configuration on a 4nm process, emphasizing AI enhancements via integrated NPUs and larger caches up to 32 MB L3 per chiplet. Intel's Meteor Lake, launched in late 2023 as the first tiled consumer CPU, integrates compute, graphics, SoC, and I/O tiles on Intel 4 and other nodes, marking a shift to disaggregated designs for better scalability. Apple's M3 Ultra, a dual-die SoC unveiled in 2025 for high-end Macs, achieves 184 billion transistors by fusing two M3 Max dies with UltraFusion interconnects, supporting up to 128 GPU cores and 128GB unified memory for professional workloads. Recent advancements include Apple's M4 series in 2025, with the M4 Max featuring estimated counts exceeding 100 billion transistors per die, pushing toward over 200 billion in dual-die Ultra configurations. These designs underscore the convergence toward heterogeneous integration, where transistor budgets allocate resources dynamically for general computing demands, projecting continued growth to exceed 200 billion in single-package CPUs by the late 2020s.Graphics Processing Units
Graphics processing units (GPUs) are specialized integrated circuits designed for parallel processing tasks, particularly in rendering graphics and accelerating artificial intelligence workloads, where high transistor counts enable massive arrays of processing elements such as shaders and tensor cores. Shaders, often referred to as CUDA cores in NVIDIA architectures or stream processors in AMD designs, form the core of GPU parallelism, handling vertex, pixel, and compute operations; their proliferation significantly contributes to transistor budgets, with modern high-end GPUs featuring tens of thousands of such units to support real-time rendering and simulations. Tensor cores, introduced by NVIDIA in the Volta architecture and evolved in subsequent generations, are dedicated hardware for matrix multiply-accumulate operations central to deep learning, adding specialized circuitry that boosts transistor density for AI tasks like training large language models.[43] Discrete GPUs, standalone chips optimized for peak performance in dedicated graphics cards, achieve far higher transistor counts than integrated GPUs embedded within system-on-chip designs for general computing; for instance, discrete models can exceed 90 billion transistors, while integrated variants typically range from hundreds of millions to a few billion, limited by power and area constraints in CPUs or APUs. This distinction arises because discrete GPUs prioritize raw compute throughput for graphics and AI, incorporating extensive shader arrays and tensor cores without the space-sharing compromises of integrated solutions.[44] NVIDIA's Blackwell architecture, launched in 2024, exemplifies escalating transistor integration with its B100 accelerator featuring 208 billion transistors across a dual-die configuration, where each die holds 104 billion transistors fabricated on TSMC's 4NP process, enabling unprecedented AI performance through enhanced tensor core capabilities. The consumer-oriented GeForce RTX 5090, based on the same GB202 die and released in 2025, packs 92.2 billion transistors, supporting over 3,352 trillion AI operations per second via fourth-generation tensor cores and a vast shader array of 21,760 units. AMD's RDNA 4 architecture, introduced in 2025, powers the Navi 48 GPU in the Radeon RX 9070 XT with 53.9 billion transistors on a 357 mm² die using TSMC's 4nm process, achieving higher density than comparable NVIDIA chips while emphasizing ray tracing and AI upscaling through optimized compute units.[45][46][47] AI acceleration has propelled GPU transistor counts upward, with architectures like Blackwell's single-die 104 billion transistors underscoring the shift toward specialized AI hardware that demands exponential scaling to handle trillion-parameter models. This trend extends to multi-chip modules, as seen in NVIDIA's projected Vera Rubin superchip for 2026, which aggregates up to six trillion transistors across multiple dies and high-bandwidth memory stacks, forming AI supercomputers that dwarf single-die gaming GPUs like the 92-billion-transistor RTX 5090. Such advancements reflect broader industry momentum, where AI workloads drive transistor densities beyond traditional graphics rendering, projecting trillion-scale GPUs within a decade to meet compute demands.[8][48]Field-Programmable Gate Arrays
Field-programmable gate arrays (FPGAs) are reconfigurable integrated circuits whose transistor counts encompass the programmable elements that enable flexibility in logic implementation, including configurable logic blocks (CLBs), programmable interconnects, and specialized digital signal processing (DSP) blocks. CLBs form the core computational units, each typically comprising look-up tables (LUTs), flip-flops, and multiplexers to realize custom logic functions, with the underlying transistors—including SRAM cells for configuration and pass transistors for routing—contributing significantly to the overall count. Programmable interconnects, which link CLBs and other resources via switch matrices and routing channels, rely on dense arrays of multiplexers and buffers, often accounting for 60-70% of the total transistors due to their extensive wiring needs. DSP blocks, integrated for efficient arithmetic operations like multiplication and accumulation, incorporate hardened multipliers and adders, adding thousands of transistors per block to support signal processing tasks without relying solely on soft logic.[49][50][51] A prominent example is the Xilinx Virtex UltraScale+ VU19P, introduced in 2019 on a 16 nm process, which features 35 billion transistors across 9 million system logic cells, enabling high-density emulation and prototyping applications. Following AMD's 2022 acquisition of Xilinx, the Versal Premium series advanced this architecture, with the VP1802 device reaching 50 billion transistors on a 7 nm process, incorporating enhanced DSP slices and AI engines for accelerated computing. These counts reflect the inclusion of configurable transistors in the fabric, allowing post-fabrication reconfiguration for diverse uses like custom accelerators.[52][53][54] Trends in FPGA transistor counts emphasize integration of hard ARM processor cores within system-on-chip (SoC) variants, such as the AMD Zynq series, to combine programmable logic with embedded processing for hybrid designs in AI and edge computing, while maintaining flexibility over fixed ASICs. Modern FPGAs, fabricated on nodes like 7 nm and 6 nm, achieve high densities for rapid prototyping of complex systems, though their transistor counts remain lower than equivalent ASICs due to the overhead of programmability. The AMD-Xilinx merger has accelerated developments, with 2025 updates to Versal devices focusing on AI engines and increased logic density to support emerging workloads.[55][56][54]Memory Devices
Memory devices, such as static random-access memory (SRAM), dynamic random-access memory (DRAM), and NAND flash, represent a significant portion of transistor counts in integrated circuits due to their role in data storage. SRAM cells typically require six transistors per bit to maintain state without periodic refresh, providing fast access but at the cost of higher density compared to other types.[57] In contrast, DRAM cells use a single transistor paired with a capacitor to store each bit as charge, enabling higher density but necessitating refresh cycles to prevent data loss.[2] NAND flash memory employs charge trap transistors in 3D configurations, with one transistor per cell capable of storing multiple bits (e.g., 2-4 bits in multi-level cells), achieving non-volatile storage with variable transistor efficiency depending on cell type and layering.[58] Modern DRAM chips exemplify scaling in transistor counts, with Samsung's 1 terabit (Tb) DDR5 DRAM modules in the 2020s incorporating billions of transistors across stacked dies to support high-capacity applications like servers and AI systems. High-bandwidth memory (HBM) variants, such as HBM3 stacks used in graphics and AI accelerators, integrate multiple DRAM dies vertically, resulting in over 100 billion transistors per stack by 2025 through 8- to 12-high configurations that enhance bandwidth while managing thermal constraints.[15] Key trends in memory transistor counts emphasize 3D stacking to overcome planar scaling limits, allowing increased density without proportional area growth; for instance, HBM4 advancements in 2025 introduce higher layer counts and finer process nodes, projecting up to 30% bit density improvements per stack for AI workloads. In 3D NAND, transistor efficiency per bit improves with vertical layering, as seen in 200+ layer devices that boost overall chip counts into the trillions for multi-terabit capacities while optimizing power and endurance. These developments prioritize per-bit efficiency alongside total transistor scaling, balancing storage density with performance in data-intensive environments.[15][59][60]Other Integrated Circuits
Other integrated circuits encompass a diverse range of application-specific integrated circuits (ASICs) beyond traditional compute and memory devices, including those for networking, sensing, power management, and emerging technologies like AI accelerators and photonic systems. These ICs often prioritize specialized functionality, efficiency, and integration over raw computational density, resulting in transistor counts that vary widely based on application needs. For instance, networking ASICs designed for high-throughput data routing and switching, such as Broadcom's Tomahawk 4, achieve over 31 billion transistors to support 25.6 Tbps of Ethernet bandwidth across 64 ports at 400 GbE, leveraging a 7 nm process for dense SerDes integration and packet processing.[61][62] Sensors, particularly CMOS image sensors, represent another key category with transistor counts typically in the low millions to tens of billions, depending on resolution and features like event-based detection. Advanced examples, such as advanced CMOS image sensors, can reach tens of billions of transistors to enable high-speed, low-power vision processing for applications like autonomous systems. Power management ICs (PMICs), which regulate voltage and current for efficient energy distribution in portable and embedded devices, generally feature lower transistor densities in the millions, focusing on analog and mixed-signal components rather than digital logic scaling. AI accelerators tailored for non-general-purpose workloads, like Google's TPU v5, exemplify custom ASICs pushing toward 50 billion transistors to optimize tensor operations and inference at scale, with estimates reflecting advancements in systolic array designs on advanced nodes. In emerging photonic integrated circuits (PICs), which combine electronic and optical elements for high-bandwidth communication, transistor counts remain lower, often around 16 million per module, as the focus shifts to waveguide and modulator integration rather than pure electronic density.[63][64] Trends in this domain highlight the rise of custom ASICs for edge AI, where compact designs with transistor counts in the tens to hundreds of millions enable on-device inference for IoT and wearables, balancing performance with power constraints. Similarly, multi-die automotive SoCs are increasingly adopted to integrate diverse functions like ADAS and infotainment, effectively scaling transistor equivalents beyond monolithic limits through chiplet architectures, though specific counts vary by vendor and remain in the billions per package. These developments underscore a shift toward heterogeneous integration, enhancing reliability and cost-efficiency in specialized applications.Historical Milestones
Early Transistor Computers
The pioneering computers of the 1950s marked a pivotal shift from vacuum tube-based systems to transistorized designs, dramatically improving reliability and reducing physical size and power consumption. The ENIAC, completed in 1945, relied on approximately 18,000 vacuum tubes, which occupied 1,800 square feet, consumed 150 kilowatts of power, and required frequent maintenance due to tube failures every few hours. This generation's limitations in scale and dependability spurred the adoption of transistors, invented in 1947, which offered solid-state switching with far greater durability and efficiency.[65] The TRADIC (TRAnsistor DIgital Computer), developed by Bell Labs and operational in 1954, became the first fully transistorized computer, utilizing about 700 point-contact transistors and over 10,000 diodes in a compact, airborne-capable system weighing just 550 pounds and drawing only 100 watts.[65] Unlike its vacuum tube predecessors, TRADIC demonstrated enhanced reliability, with mean time between failures extending to thousands of hours, and enabled a size reduction to roughly one-fiftieth that of equivalent tube-based machines, facilitating applications in military avionics.[66] By the early 1960s, discrete transistor counts had scaled significantly; the IBM 7090, introduced in 1960, incorporated over 50,000 germanium transistors across its modules, achieving six times the performance of its vacuum tube predecessor, the IBM 709, while occupying less space and using far less power.[67] This era's transistor counts, starting in the hundreds and reaching tens of thousands, laid the groundwork for integrated circuits (ICs). The Intel 4004, released in 1971 as the first commercial microprocessor, integrated 2,300 transistors on a single silicon chip using pMOS technology, bridging discrete systems to monolithic designs and enabling programmable computing in compact devices like calculators.[6] The rapid increase from TRADIC's hundreds to the 4004's thousands exemplified early validation of scaling trends later formalized as Moore's Law.Logic Functions and Parallel Systems
In complementary metal-oxide-semiconductor (CMOS) technology, basic logic gates such as a two-input NAND gate typically require four transistors—two n-type MOSFETs in series for the pull-down network and two p-type MOSFETs in parallel for the pull-up network.[68] This configuration ensures low power consumption by allowing only one network to conduct at a time, with similar transistor efficiencies observed in other fundamental gates like NOR (also four transistors for two inputs) and inverters (two transistors).[69] As logic complexity increased from the 1980s, these basic building blocks scaled into more intricate structures; for instance, arithmetic logic units (ALUs) in early 32-bit microprocessors incorporated tens of thousands of transistors to handle operations like addition and bitwise logic, evolving from simpler designs in the Motorola 68000 (68,000 total transistors, including its 16-bit ALU) to millions in subsequent generations.[70] The advent of parallelism amplified transistor utilization by integrating multiple processing elements on a single die or across systems, enabling higher effective counts through coordinated operation. In multi-core processors, transistor budgets expanded to support replicated cores, caches, and interconnects; the Intel Core i7-940 (Nehalem architecture, 2008), a quad-core design, featured 731 million transistors, with a significant portion allocated to parallel execution units and shared resources for symmetric multiprocessing (SMP).[71] This scaling continued into clusters and SMP configurations, where multiple sockets aggregate transistor resources; for example, early parallel systems like the Cray-1 supercomputer (1976, but influential into the 1980s) employed approximately 200,000 integrated circuits, each with up to 16 emitter-coupled logic (ECL) transistors, yielding around 3.2 million transistors total for vector processing across its custom logic arrays.[72] In modern multi-socket servers, parallelism achieves effective transistor counts in the tens of billions by combining high-core-count dies; a dual-socket system using AMD EPYC processors (e.g., third-generation Milan, with ~4.15 billion transistors per chiplet-based compute die and up to 64 cores per socket) can aggregate approximately 83 billion transistors, leveraging non-uniform memory access (NUMA) for distributed computation akin to supercomputer clusters.[73][74] Architectural trends from the 1980s onward shifted from complex instruction set computing (CISC) and reduced instruction set computing (RISC) paradigms—where transistor growth primarily enhanced single-thread performance—to heterogeneous computing, incorporating specialized accelerators (e.g., GPUs or AI units) that repurpose transistor density for domain-specific parallelism, as seen in the replication of cores and heterogeneous integration driving sustained increases in overall counts.[75] This evolution prioritizes efficient resource allocation over uniform scaling, with multi-core and clustered designs transforming isolated logic functions into cohesive parallel ecosystems.[76]Records and Projections
Highest Counts Achieved
As of November 2025, the highest transistor counts in integrated circuits have been achieved primarily in advanced microprocessors, graphics processing units, and specialized AI accelerators, driven by multi-die packaging and wafer-scale integration to surpass traditional single-die limits. These milestones reflect manufacturer efforts to scale compute power for AI and high-performance computing, with counts verified through official announcements and technical specifications.[77][45][78] In microprocessors, Apple's M3 Ultra SoC holds the record for consumer devices at 184 billion transistors, achieved via an UltraFusion interconnect linking two M3 Max dies fabricated on TSMC's 3nm process. This configuration enables up to 32 CPU cores and 80 GPU cores, targeting professional workloads in the Mac Studio. For graphics processing units, Nvidia's Blackwell architecture GPUs, such as the B200, reach 208 billion transistors through a dual-die design on a custom TSMC 4NP process, with each die containing 104 billion transistors connected via high-bandwidth interfaces for AI training and inference.[77][79] Specialized non-consumer devices push boundaries further; Cerebras Systems' Wafer Scale Engine 3 (WSE-3), announced in 2024, integrates 4 trillion transistors across a full silicon wafer using TSMC's 5nm process, incorporating over 900,000 AI-optimized cores for large-scale model training in data centers. This wafer-scale approach yields the highest single-chip count to date, far exceeding traditional dies by leveraging monolithic fabrication to minimize interconnect latency.[78] Multi-die packages aggregate counts across chiplets for even greater scale, as seen in AMD's server-oriented designs. For instance, the AMD Instinct MI300X AI accelerator combines multiple chiplets on TSMC's 5nm and 6nm processes to total 153 billion transistors, supporting 304 compute units and 192 GB of HBM3 memory for high-bandwidth AI tasks. Similarly, AMD's EPYC 9005 series processors employ up to 12 Zen 5 core complex dies plus an I/O die, enabling up to 192 cores for cloud and enterprise computing. These multi-chiplet architectures allow modular scaling while managing yield challenges inherent to large single dies.[80][81] Regarding single-die records, the highest verified count in 2025 production chips approaches 104 billion transistors per die in Nvidia's Blackwell GPUs, limited by reticle size constraints on advanced nodes like TSMC 4NP. AMD and other vendors hover around 90-100 billion for their largest single dies in GPUs and accelerators, such as the 92 billion in Apple's M3 Max, underscoring the shift toward multi-die systems to exceed these thresholds without prohibitive manufacturing risks. All figures are derived from manufacturer disclosures, confirming practical achievability in commercial and research applications.[79][80]| Category | Device | Transistor Count | Year | Notes |
|---|---|---|---|---|
| Microprocessor (Consumer) | Apple M3 Ultra | 184 billion | 2025 | Dual-die SoC on 3nm |
| GPU (Data Center) | Nvidia Blackwell B200 | 208 billion | 2024 | Dual-die on 4NP |
| AI Accelerator (Wafer-Scale) | Cerebras WSE-3 | 4 trillion | 2024 | Monolithic wafer on 5nm |
| AI Accelerator (Multi-Chiplet) | AMD Instinct MI300X | 153 billion | 2023 | Chiplets on 5nm/6nm |
| Server CPU (Multi-Chiplet) | AMD EPYC 9005 | Multi-chiplet (up to 12 compute dies) | 2024 | Up to 192 cores on 4nm/5nm |
| Single Die (Highest) | Nvidia Blackwell Die | 104 billion | 2024 | Per die in dual configuration |
Future Trends
Industry projections anticipate transistor counts surpassing one trillion in multi-chiplet graphics processing units by the early 2030s, driven by advancements in 3D stacking and chiplet-based architectures that enable modular integration of high-density dies. For example, NVIDIA's Vera Rubin superchip, expected in late 2026, will achieve six trillion transistors through interconnected chiplets featuring multiple reticle-sized GPUs and extensive high-bandwidth memory stacks. Similarly, TSMC forecasts that multi-chiplet GPUs will exceed one trillion transistors within a decade from 2024, leveraging 3D integration to connect numerous chiplets in stacked configurations. Intel and TSMC are targeting 1nm-equivalent process nodes by 2030, which could support monolithic chips with up to 200 billion transistors, further amplifying system-level counts when combined with packaging innovations.[82][8][83] Emerging transistor technologies are pivotal to these projections, with complementary field-effect transistors (CFET) enabling vertical stacking of n-type and p-type channels to reduce footprint and boost density beyond current gate-all-around nanosheet devices, slated for deployment at the 1nm node around 2028. Quantum dot-based transistors represent another frontier, offering potential for room-temperature operation and surpassing silicon limits in speed and efficiency through mixed-valence molecular structures that could replace traditional switches in next-generation logic. These innovations, alongside 3D packaging, mitigate the slowing of Moore's Law by shifting scaling from planar dimensions to vertical and architectural enhancements.[84][85] Transistor density scaling can be approximated by the equationwhere is the number of technology generations (assuming approximately two generations per doubling period under historical Moore's Law trends), leading to roughly doubling of density every two generations. However, realizing trillion-scale counts introduces formidable challenges, including severe heat dissipation issues as transistor proximity intensifies local hotspots and power density rises in 3D stacks. Manufacturing costs also escalate dramatically, with advanced nodes and specialized memory like HBM driving up expenses per transistor and limiting economic viability without yield improvements.[86][87][88]
Related Metrics
Gate Count
Gate count is a metric used to quantify the logic complexity of an integrated circuit by estimating the number of equivalent logic gates, typically standardized as two-input NAND gate equivalents (GE). In complementary metal-oxide-semiconductor (CMOS) technology, a basic two-input NAND gate requires four transistors (two PMOS and two NMOS), while simpler elements like an inverter use two transistors (equivalent to 0.5 GE) and more complex ones like a two-input XOR may use 12 transistors (equivalent to 3 GE). This results in a typical range of 2 to 6 transistors per gate equivalent, depending on the gate type and implementation.[89][90] To convert from total transistor count to gate count , designers approximate , where is the average transistors per gate, commonly 4 for CMOS logic in standard cells. This conversion is widely applied in field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) design to assess logic resource utilization and compare architectures across technologies, providing a technology-independent measure of design scale.[91][92] Unlike raw transistor count, which encompasses all active devices on a chip, gate count emphasizes functional logic complexity by focusing on combinational and sequential elements, such as those in arithmetic units or control logic. It excludes transistors in non-logic components like embedded memory arrays (e.g., SRAM blocks), input/output pads, and clock distribution networks, allowing for a clearer evaluation of computational capability without the influence of support circuitry. This distinction is particularly valuable in hardware efficiency assessments, such as for cryptographic primitives, where logic gate minimization directly impacts area, power, and performance.[93][94]Complexity Equivalents
Transistor count provides a foundational proxy for assessing the computational complexity of integrated circuits, often correlated with operational throughput metrics such as gigaflops (GFLOPS) or teraflops (TFLOPS) in graphics processing units (GPUs). In modern GPU architectures, transistor count and performance in low-precision floating-point operations relevant to AI workloads show a rough correlation that evolves with process node advancements and design optimizations.[95] Die area serves as another key equivalent, acting as a direct indicator of transistor density at a fixed manufacturing process, where larger areas enable higher counts and thus greater potential for parallel processing capabilities.[96] Performance comparisons highlight that transistor count does not scale linearly with computational output due to architectural variations, such as the inclusion of specialized tensor cores or memory hierarchies. For instance, NVIDIA's Blackwell GPU, featuring 208 billion transistors, achieves up to 20 petaFLOPS (20,000 TFLOPS) in AI-optimized FP4 precision as of 2024, demonstrating how efficiency gains amplify throughput far beyond proportional increases in transistor numbers.[45] Gate equivalents provide a complementary view, as multiple transistors typically form a single logic gate, offering a bridge between raw count and functional logic density.[2] Despite these correlations, raw transistor count has notable limitations as a performance metric, failing to account for utilization efficiency or workload specificity. Specialized older chips with fewer transistors can outperform more complex modern designs in niche tasks, such as low-power embedded applications, where architectural tailoring prioritizes targeted operations over sheer scale.[97] This underscores that transistor proliferation alone does not guarantee superior results, as power delivery, interconnects, and software optimization play critical roles in realizing potential.[96] In emerging AI accelerators, metrics like tera-operations per second (TOPS) per transistor are increasingly used to quantify efficiency, emphasizing how effectively transistors contribute to inference and training tasks amid rising demands for sparse and quantized computations. For example, advanced edge AI chips target high TOPS densities to balance transistor budgets with real-time performance, reflecting a shift toward metrics that prioritize energy-aware utilization over absolute counts.[8]References
- https://en.wikichip.org/wiki/mtr-mm%25C2%25B2
