In this article, we describe the 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 improvements to software-stack performance and scalability, showing a 1.3× speedup in the top 16-chip results despite higher quality targets and a 5.5× increase in system scale. The first round of MLPerf Inference received over 500 benchmark results from 14 different organizations, showing growing adoption.
ASJC Scopus subject areas
- Hardware and Architecture
- Electrical and Electronic Engineering