Hardware acceleration is the use of computer hardware, known as a hardware accelerator, to perform specific functions faster than can be done by software running on a general-purpose central processing unit (CPU). Any transformation of data that can be calculated by software running on a CPU can also be calculated by an appropriate hardware accelerator, or by a combination of both.

To perform computing tasks more efficiently, generally one can invest time and money in improving the software, improving the hardware, or both. There are various approaches with advantages and disadvantages in terms of decreased latency, increased throughput, and reduced energy consumption.

Typical advantages of focusing on software may include greater versatility, more rapid development, lower non-recurring engineering costs, heightened portability, and ease of updating features or patching bugs, at the cost of overhead to compute general operations.

Advantages of focusing on hardware may include speedup, reduced power consumption, lower latency, increased parallelism and bandwidth, and better utilization of area and functional components available on an integrated circuit; at the cost of lower ability to update designs once etched onto silicon and higher costs of functional verification, times to market, and the need for more parts.

In the hierarchy of digital computing systems ranging from general-purpose processors to fully customized hardware, there is a tradeoff between flexibility and efficiency, with efficiency increasing by orders of magnitude when any given application is implemented higher up that hierarchy. This hierarchy includes general-purpose processors such as CPUs, more specialized processors such as programmable shaders in a GPU, applications implemented on field-programmable gate arrays (FPGAs), and fixed-function implemented on application-specific integrated circuits (ASICs).

Hardware acceleration is advantageous for performance, and practical when the functions are fixed, so updates are not as needed as in software solutions. With the advent of reprogrammable logic devices such as FPGAs, the restriction of hardware acceleration to fully fixed algorithms has eased since 2010, allowing hardware acceleration to be applied to problem domains requiring modification to algorithms and processing control flow. The disadvantage, however, is that in many open source projects, it requires proprietary libraries that not all vendors are keen to distribute or expose, making it difficult to integrate in such projects.

Integrated circuits are designed to handle various operations on both analog and digital signals. In computing, digital signals are the most common and are typically represented as binary numbers. Computer hardware and software use this binary representation to perform computations. This is done by processing Boolean functions on the binary input, and then outputting the results for storage or further processing by other devices.

Because all Turing machines can run any computable function, it is always possible to design custom hardware that performs the same function as a given piece of software. Conversely, software can always be used to emulate the function of a given piece of hardware. Custom hardware may offer higher performance per watt for the same functions that can be specified in software. Hardware description languages (HDLs) such as Verilog and VHDL can model the same semantics as software and synthesize the design into a netlist that can be programmed to an FPGA or composed into the logic gates of an ASIC.