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MLPerf Debuts Inference Benchmarks

November 26, 2019

Author: Bob Wheeler

Start with an unbounded problem and you’ll never find a solution. Such was the MLPerf group’s quandary in developing machine-learning-inference benchmarks. Pragmatism won out, and the group released a modest set of five benchmarks, focusing heavily on vision processing. It also defined client, edge, and cloud use cases, however, for 20 total combinations. It included tightly controlled and user-defined implementations as well, trading reproducibility against innovation.

Among the first results are submissions from 14 organizations comprising cloud-service providers (CSPs), server vendors, chip vendors, and professional-services companies. Smartphone OEMs are notably absent, but AI-Benchmark also serves that segment. The MLPerf submissions include Alibaba, Google, Habana, Intel, and Nvidia data-center accelerators; Intel Core and Xeon processors; and Nvidia and Qualcomm client/edge SoCs. The newest designs stood out in vision-processing performance, but incumbents demonstrated broader support across neural-networks types.

MLPerf Inference v0.5 carries over a problem we noted with the group’s Training v0.5 benchmark: it doesn’t require submissions to include minimum sets of benchmarks. As a consequence, the initial results form a sparse matrix that limits direct product comparisons. Still, the benchmark should enable more-reproducible performance testing and more-verifiable vendor claims.

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