… benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore
present MLPerf, an ML benchmark … the first two MLPerf benchmarking rounds, along with …

… -34 backbone, which also comes from our training benchmark. The lightweight object detector’s
… ) architecture and is part of the training benchmark. RNNs are popular for sequential and …

… benchmark for these systems. To meet this need, we present MLPerf Tiny, the first
industry-standard benchmark suite for … The benchmark suite is the collaborative effort of more than …

… on the MLPerf v0.5 training benchmark suite. We evaluate the MLPerf benchmarks with …
Additionally, we investigate whether the execution characteristics of these benchmarks point out …

… design choices behind MLPerf, a machine learning performance benchmark that has become
an industry standard. The first two rounds of the MLPerf Training benchmark helped drive …

… an industry-relevant recommendation benchmark for the MLPerf Training and Inference suites.
… The benchmark will reflect standard practice to help customers choose among hardware …

… between system scale, training time, and energy … released MLPerf Training power benchmarks.
Figure 6 shows the time-to-train and energy consumption metrics from our v4.0 training …

… We systematically collected MLPerf training benchmark results2 from versions 0.5 through
… across 15 benchmark categories and 69 hardware configurations. By parsing MLPerf’s …

… suite with proper metrics and benchmarking methodologies … In this article, we describe the
MLPerf Inference benchmark’s … original MLPerf Inference Benchmark13 and MLPerf Training …

… Therefore, we chose the minimum-accuracy thresholds (“Quality Target” in Table 1) on the
basis of what is achievable through post-training quantization without any training data. For …