Benchmarking Deep Learning Hardware and Frameworks: Qualitative Metrics Benchmarking Deep Learning Hardware and Frameworks: Qualitative Metrics
Paper summary Benchmarking Deep Learning Hardware and Frameworks: Qualitative Metrics Previous papers on benchmarking deep neural networks 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 qualitatively compares these technologies with respect to benchmarking from the angles of our 7-metric approach to deep learning frameworks and 12-metric approach to machine learning hardware platforms. After reading the paper, the audience will understand seven benchmarking principles, generally know that differential characteristics of mainstream artificial intelligence devices, qualitatively compare deep learning hardware through the 12-metric approach for benchmarking neural network hardware, and read benchmarking results of 16 deep learning frameworks via our 7-metric set for benchmarking frameworks.
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Benchmarking Deep Learning Hardware and Frameworks: Qualitative Metrics
Wei Dai and Daniel Berleant
arXiv e-Print archive - 2019 via Local arXiv
Keywords: cs.DC, cs.LG, cs.PF

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Summary by Wei Dai 1 week ago
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