Investigating the Impact of Non-Volatile Memories on Energy-Efficiency of Coarse-Grained Reconfigurable Architectures

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

Coarse-Grained Reconfigurable Architectures (CGRAs) are promising solutions to achieve more performance with the end of Moore's law. CGRAs can provide flexibility as well as near-ASIC energy efficiency. Since the advent of IoT and battery-powered edge devices, energy efficiency is becoming increasingly important. Memory accesses contribute to about 50% of overall energy consumption of the CGRAs. Interesting features of emerging non-volatile memories (eNVMs) like low power consumption and high density have grown the attentions. In this work, the effect of eNVMs on energy efficiency of CGRAs have been investigated. The analysis using Polybench benchmark suite shows that STT-MRAM and PCM can result in a 94 % and 85 % reduction of energy consumption of memory accesses respectively compared to SRAM. Moreover, total access latency can also be improved by 60% and 49% in STT-MRAM and PCM.

Details

OriginalspracheEnglisch
TitelProceedings - 2023 26th Euromicro Conference on Digital System Design, DSD 2023
Redakteure/-innenSmail Niar, Hamza Ouarnoughi, Amund Skavhaug
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten748-755
Seitenumfang8
ISBN (elektronisch)979-8-3503-4419-6
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Publikationsreihe

ReiheEuromicro Symposium on Digital System Design (DSD)
ISSN2639-3859

Konferenz

Titel26th Euromicro Conference on Digital System Design
KurztitelDSD 2023
Veranstaltungsnummer26
Dauer6 - 8 September 2023
Webseite
OrtGrand Blue Fafa Resort
StadtDurres
LandAlbanien

Externe IDs

ORCID /0000-0002-8019-7936/work/159606324
ORCID /0000-0003-2571-8441/work/159607556

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Coarse-Grained Reconfigurable Architectures (CGRAs), Emerging Non-Volatile Memory (eNVM), Energy Efficiency, Machine Learning