Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. As of 2023, the market for AI hardware is dominated by GPUs.[1]
See main article: Lisp machine. Lisp machines were developed in the late 1970s and early 1980s to make Artificial intelligence programs written in the programming language Lisp run faster.
See main article: Dataflow architecture. Dataflow architecture processors used for AI serve various purposes, with varied implementations like the polymorphic dataflow[2] Convolution Engine[3] by Kinara (formerly Deep Vision), structure-driven dataflow by Hailo,[4] and dataflow scheduling by Cerebras.[5]
See main article: AI accelerator.
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.[6] 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.[7] 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.[8] [9]