We will also examine how the nature of the input data (regular grid, sequence, graph, unstructured) influences the DNN model architectures and their choice of operations. Finally, we will outline how the workload of a given network differs between its training and inference, due to changes in input characteristics, operation fusions, and workload-reduction techniques such as quantization and sparsity. The MLPerf Training and Inference Benchmarks have become the industry standard for measuring machine learning system performance (speed). We will describe the design choices in benchmarking machine learning performance, and how the MLPerf Training and Inference Benchmarks navigate those choices. We will walk through the submission and review process for the benchmarks, with the goal of enabling smooth submissions for potential submitters. We will review industry progress as shown by 2+ years of results on the benchmark suites. Lastly, we will discuss ongoing work to improve the benchmark suites, and how new collaborators can become involved and make a field-wide impact.
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