Survey of Machine Learning Accelerators

• URL: http://arxiv.org/abs/2009.00993v1
• Date: Tue, 1 Sep 2020 01:28:59 GMT
• Title: Survey of Machine Learning Accelerators
• Authors: Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi and Jeremy Kepner
• Abstract summary: This paper updates the survey of of AI accelerators and processors from last year's IEEE-HPEC paper. This paper collects and summarizes the current accelerators that have been publicly announced with performance and power consumption numbers.
• Score: 15.163544680926474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: New machine learning accelerators are being announced and released each 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 of AI accelerators and processors from last year's IEEE-HPEC paper. This paper collects and summarizes the current accelerators that have been publicly announced with 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 discussed and analyzed. For instance, there are interesting trends in the plot regarding power consumption, numerical precision, and inference versus training. This year, there are many more announced accelerators that are implemented with many more architectures and technologies from vector engines, dataflow engines, neuromorphic designs, flash-based analog memory processing, and photonic-based processing.

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