Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite

Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite

This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns...

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Bibliographic Details
Main Authors: Fabrizio Maria Aymone, Danilo Pietro Pau
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Information
Subjects:
edge artificial intelligence
in-sensor machine learning computing
digital signal processing
intelligent signal processing unit
tiny sensors
MLCommons-Tiny working group
Online Access:https://www.mdpi.com/2078-2489/15/11/674
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