Work

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

Year: 2020

Type: conference-paper

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.

Source: HAL

Authors: Guillaume Devic, Gilles Sassatelli, Abdoulaye Gamatié

Language: English

Topic: Embedded Systems Design Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, Région Occitanie Pyrénées-Méditerranée

Award: Design Continuum for Next Generation Energy-Efficient Compute Nodes

OpenAlex ID: W3158350436

FWCI:

Cites: 1

Cited by: 1

Related to: 10