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Conference Papers Year : 2020

Energy-Efficient Machine Learning on FPGA for Edge Devices: a Case Study

Guillaume Devic
Gilles Sassatelli
Abdoulaye Gamatié

Abstract

This paper presents a case study on the combination of a few static code optimization with an FPGA prototype of heterogeneous multicore architecture to address the energy-efficient execution of machine learning algorithms at the edge computing nodes. Two kinds of optimizations are applied : usual compiler optimizations and real number representations (fixed-point versus floating-point). This study is conducted while accounting for the trade-off between training precision, performance, and energy.
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Dates and versions

lirmm-03041276 , version 1 (04-12-2020)
lirmm-03041276 , version 2 (02-01-2021)

Identifiers

  • HAL Id : lirmm-03041276 , version 2

Cite

Guillaume Devic, Gilles Sassatelli, Abdoulaye Gamatié. Energy-Efficient Machine Learning on FPGA for Edge Devices: a Case Study. ComPAS 2020 - Conférence en Parallélisme, Architecture et Système, Jun 2020, Lyon, France. ⟨lirmm-03041276v2⟩
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