  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 benchmark organization 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.
