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This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages) This article needs attention from an expert in Artificial intelligence. The specific problem is: Needs attention from a current expert to incorporate modern developments in this area from the last few decades, including TPUs and better coverage of GPUs, and to clean up the other material and clarify how it relates to the subject. WikiProject Artificial intelligence may be able to help recruit an expert. (November 2021) This article is missing information about its scope: What is AI hardware for the purposes of this article? Event cameras are an application of neuromorphic design, but LISP machines are not an end use application. It previously mentioned memristors, which are not specialized hardware for AI, but rather a basic electronic component, like resister, capacitor, or inductor. Please expand the article to include this information. Further details may exist on the talk page. (November 2021) This article needs to be updated. Please help update this article to reflect recent events or newly available information. (November 2021) (Learn how and when to remove this message)

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.

Lisp machines

Lisp machines were developed in the late 1970s and early 1980s to make Artificial intelligence programs written in the 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, and dataflow 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

1. "Nvidia: The chip maker that became an AI superpower". BBC News. 25 May 2023. Retrieved 18 June 2023.
2. Maxfield, Max (24 December 2020). "Say Hello to Deep Vision's Polymorphic Dataflow Architecture". Electronic Engineering Journal. Techfocus media.
3. "Kinara (formerly Deep Vision)". Kinara. 2022. Retrieved 2022-12-11.
4. "Hailo". Hailo. Retrieved 2022-12-11.
5. Lie, Sean (29 August 2022). Cerebras Architecture Deep Dive: First Look Inside the HW/SW Co-Design for Deep Learning. Cerebras (Report).
6. Research, AI (23 October 2015). "Deep Neural Networks for Acoustic Modeling in Speech Recognition". AIresearch.com. Retrieved 23 October 2015.
7. Kobielus, James (27 November 2019). "GPUs Continue to Dominate the AI Accelerator Market for Now". InformationWeek. Retrieved 11 June 2020.
8. Tiernan, Ray (2019). "AI is changing the entire nature of compute". ZDNet. Retrieved 11 June 2020.
9. "AI and Compute". OpenAI. 16 May 2018. Retrieved 11 June 2020.

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