TL;DR
This survey reviews recent developments in AI accelerators, analyzing performance and power trends, and compiles benchmarking results to understand efficiency improvements over the past two years.
Contribution
It provides an updated comprehensive overview of commercial AI accelerators, including performance, power consumption, and efficiency trends, with new benchmarking analysis.
Findings
Performance and power consumption vary widely among accelerators.
Trends show increasing efficiency and specialization.
Benchmarking results highlight areas for future improvement.
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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