[Embench] TinyML MLPerf results

David PATTERSON pattrsn at cs.berkeley.edu
Wed Apr 6 19:47:20 CEST 2022


The MLPerf Tiny benchmark suite is intended for the lowest power devices
and smallest form factors, such as deeply embedded, intelligent sensing,
and internet-of-things applications. The second round of MLPerf Tiny
results showed tremendous growth in collaboration with submissions from
Alibaba, Andes, hls4ml-FINN team, Plumerai, Renesas, Silicon Labs,
STMicroelectronics, and Syntiant. Collectively, these organizations
submitted 19 different systems with 3X more results than the first round
and over half the results incorporating energy measurements, an impressive
achievement for the first benchmarking round with energy measurement.

To view the results and find additional information about the benchmarks
please visit https://mlcommons.org/en/inference-datacenter-20/,

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