Specialized

computer hardware Computer hardware includes the physical parts of a computer, such as the central processing unit (CPU), random-access memory (RAM), motherboard, computer data storage, graphics card, sound card, and computer case. It includes external devices ...

is often used to execute

artificial intelligence Artificial intelligence (AI) is the capability of computer, computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of re ...

(AI) programs faster, and with less energy, such as Lisp machines,

neuromorphic engineering Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term ...

event camera An event camera, also known as a neuromorphic camera, silicon retina, or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional (frame) cam ...

s, and physical neural networks. Since 2017, several consumer grade

CPU A central processing unit (CPU), also called a central processor, main processor, or just processor, is the primary processor in a given computer. Its electronic circuitry executes instructions of a computer program, such as arithmetic, log ...

s and system on a chip, SoCs have on-die AI accelerator, NPUs. As of 2023, the market for AI hardware is dominated by GPUs.

Lisp machines

Lisp machines were developed in the late 1970s and early 1980s to make Artificial intelligence programs written in the programming language Lisp (programming language), Lisp run faster.

Dataflow architecture

Dataflow architecture processors used for AI serve various purposes with varied implementations like the polymorphic dataflow Convolution Engine by Kinara (formerly Deep Vision), structure-driven dataflow by Hailo Technologies, Hailo, and dataflow Scheduling (computing), scheduling by Cerebras.

Component hardware

AI accelerators

Since the 2010s, advances in computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced central processing units (CPUs) as the dominant means to train large-scale commercial cloud AI. OpenAI estimated the hardware compute used in the largest deep learning projects from Alex Net (2012) to Alpha Zero (2017), and found a 300,000-fold increase in the amount of compute needed, with a doubling-time trend of 3.4 months.

Sources

{{Reflist Computer hardware Artificial intelligence