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MLCommons Releases MLPerf Tiny Inference Benchmark June 16, 2021 SAN FRANCISCO, June 16, 2021 Today, MLCommons, an open engineering consortium, launched a new benchmark, MLPerf Tiny Inference, to measure how quickly a trained neural network can process new data for extremely low-power devices in the smallest form factors and included an optional power measurement. MLPerf Tiny v0.5 is the organization’s first inference benchmark suite that targets machine learning use cases on embedded devices. Embedded machine learning is a burgeoning field where AI-driven sensor data analytics is performed in real-time, close to where the data resides. The new MLPerf Tiny Inference benchmark suite captures a variety of use cases that involve “tiny” neural networks, typically 100 kB and below, that process sensor data such as audio and vision to provide endpoint intelligence.

Colby banburyPeter torelliVijay janapa reddiMicroprocessor benchmark consortiumHarvard universityTiny inferencePengcheng labsNew measurementAdvance intelligenceEmbedded microprocessor benchmark consortiumWake wordsImage classificationProfessor vijay janapa reddiDigital catapultPeng cheng laboratoriesSambanova systems