AI Accelerator Survey and Trends

• URL: http://arxiv.org/abs/2109.08957v1
• Date: Sat, 18 Sep 2021 15:57:47 GMT
• Title: AI Accelerator Survey and Trends
• Authors: Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner
• Abstract summary: This paper updates the survey of AI accelerators and processors from past two years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers.
• Score: 4.722078109242797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past several years, new machine learning accelerators were being announced and released every month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications. This paper updates the survey of AI accelerators and processors from past two years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers. The performance and power values are plotted on a scatter graph, and a number of dimensions and observations from the trends on this plot are again discussed and analyzed. This year, we also compile a list of benchmarking performance results and compute the computational efficiency with respect to peak performance.

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