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

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

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

Original languageEnglish
Title of host publicationProceedings - 2023 26th Euromicro Conference on Digital System Design, DSD 2023
EditorsSmail Niar, Hamza Ouarnoughi, Amund Skavhaug
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages748-755
Number of pages8
ISBN (electronic)979-8-3503-4419-6
Publication statusPublished - 2023
Peer-reviewedYes

Publication series

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

Conference

Title26th Euromicro Conference on Digital System Design
Abbreviated titleDSD 2023
Conference number26
Duration6 - 8 September 2023
Website
LocationGrand Blue Fafa Resort
CityDurres
CountryAlbania

External IDs

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

Keywords

Sustainable Development Goals

Keywords

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