Previous survey papers offer knowledge of deep learning hardware devices and software frameworks. This paper introduces benchmarking principles, surveys machine learning devices including GPUs, FPGAs, and ASICs, and reviews deep learning software frameworks. It also reviews these technologies with respect to benchmarking from the angles of our 7-metric approach to frameworks and 12-metric approach to hardware platforms. After reading the paper, the audience will understand seven benchmarking principles, generally know that differential characteristics of mainstream AI devices, qualitatively compare deep learning hardware through our 12-metric approach for benchmarking hardware, and read benchmarking results of 16 deep learning frameworks via our 7-metric set for benchmarking frameworks.
This paper surveys benchmarking principles, machine learning devices including GPUs, FPGAs, and ASICs, and deep learning software frameworks. It also reviews these technologies with respect to benchmarking from the perspectives of a 6-metric approach to frameworks and an 11-metric approach to hardware platforms. Because MLPerf is a URLanization working with industry and academia, and offering deep learning benchmarks that evaluate training and inference on deep learning hardware devices, the survey also mentions MLPerf benchmark results, benchmark metrics, datasets, deep learning frameworks and algorithms. We summarize seven benchmarking principles, differential characteristics of mainstream AI devices, and qualitative comparison of deep learning hardware and frameworks.
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Previous survey papers offer knowledge of deep learning hardware devices and software frameworks. This paper introduces
This paper surveys
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surveys
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reviews
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angles of our 7-metric
perspectives of a 6-metric
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12-metric
an 11-metric
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After reading the paper, the audience will understand
Because MLPerf is a URLanization working with industry and academia, and offering deep learning benchmarks that evaluate training and inference on deep learning hardware devices, the survey also mentions MLPerf benchmark results, benchmark metrics, datasets, deep learning frameworks and algorithms. We summarize
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generally know that
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qualitatively compare deep learning hardware through our 12-metric approach for benchmarking hardware, and read benchmarking results of 16 deep learning frameworks via our 7-metric set for benchmarking
and qualitative comparison of deep learning hardware and