Hardware for artificial intelligence
 | 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)
|
Specialized hardware for artificial intelligence is used to execute artificial intelligence programs faster, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks.
Lisp machines
Neural network hardware
Physical neural networks
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.[1] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[2] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[3][4]
Sources
- ^ Research, AI (23 October 2015). "Deep Neural Networks for Acoustic Modeling in Speech Recognition". airesearch.com. Retrieved 23 October 2015.
- ^ "GPUs Continue to Dominate the AI Accelerator Market for Now". InformationWeek. December 2019. Retrieved 11 June 2020.
- ^ Ray, Tiernan (2019). "AI is changing the entire nature of compute". ZDNet. Retrieved 11 June 2020.
- ^ "AI and Compute". OpenAI. 16 May 2018. Retrieved 11 June 2020.
 | This computer hardware article is a stub. You can help Wikipedia by expanding it. |