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Execution time prediction for energy-efficient hardware accelerators

Authors:

Alexander Rucker,

G. Edward SuhAuthors Info & Claims

MICRO-48: Proceedings of the 48th International Symposium on Microarchitecture

Pages 457 - 469

https://doi.org/10.1145/2830772.2830798

Published: 05 December 2015 Publication History

Abstract

Many mobile applications utilize hardware accelerators for computation-intensive tasks. Often these tasks involve real-time user interactions and must finish within a certain amount of time for smooth user experience. In this paper, we propose a DVFS framework for hardware accelerators involving real-time user interactions. The framework automatically generates a predictor for each accelerator that predicts its execution time, and sets a DVFS level to just meet the response time requirement. Our evaluation results show, compared to running each accelerator at a constant frequency, our DVFS framework achieves 36.7% energy savings on average across a set of accelerators, while only missing 0.4% of the deadlines. The energy savings are only 3.8% less than an optimal DVFS scheme. We show with the introduction of a boost level, the deadline misses can be completely eliminated while still achieving 36.4% energy savings.

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Boroujerdian BJing YTripathy DKumar ASubramanian LYen LLee VVenkatesan VJindal AShearer RReddi V(2023)FARSI: An Early-stage Design Space Exploration Framework to Tame the Domain-specific System-on-chip ComplexityACM Transactions on Embedded Computing Systems10.1145/354401622:2(1-35)Online publication date: 24-Jan-2023
https://dl.acm.org/doi/10.1145/3544016
Xu YHe TSun RMa YJin YZou AMitra TYoung EXiong J(2022)SHAPEProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3549409(1-9)Online publication date: 30-Oct-2022
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D. MS. R(2020)Deep LearningDeep Learning Techniques and Optimization Strategies in Big Data Analytics10.4018/978-1-7998-1192-3.ch008(124-141)Online publication date: 2020
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Index Terms

Execution time prediction for energy-efficient hardware accelerators
1. Computer systems organization
  1. Architectures
    1. Other architectures

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cover image ACM Conferences

MICRO-48: Proceedings of the 48th International Symposium on Microarchitecture

December 2015

787 pages

ISBN:9781450340342

DOI:10.1145/2830772

General Chair:
Milos Prvulovic
Georgia Tech

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

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Published: 05 December 2015

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Office of Naval Research (ONR)

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MICRO-48

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MICRO-48: The 48th Annual IEEE/ACM International Symposium of Microarchitecture

December 5 - 9, 2015

Waikiki, Hawaii

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MICRO-48 Paper Acceptance Rate 61 of 283 submissions, 22%;

Overall Acceptance Rate 484 of 2,242 submissions, 22%

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Cited By

Boroujerdian BJing YTripathy DKumar ASubramanian LYen LLee VVenkatesan VJindal AShearer RReddi V(2023)FARSI: An Early-stage Design Space Exploration Framework to Tame the Domain-specific System-on-chip ComplexityACM Transactions on Embedded Computing Systems10.1145/354401622:2(1-35)Online publication date: 24-Jan-2023
https://dl.acm.org/doi/10.1145/3544016
Xu YHe TSun RMa YJin YZou AMitra TYoung EXiong J(2022)SHAPEProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3549409(1-9)Online publication date: 30-Oct-2022
https://dl.acm.org/doi/10.1145/3508352.3549409
D. MS. R(2020)Deep LearningDeep Learning Techniques and Optimization Strategies in Big Data Analytics10.4018/978-1-7998-1192-3.ch008(124-141)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1192-3.ch008
Calicchia LCiotoli VCardarilli Gdi Nunzio LFazzolari RNannarelli ARe M(2019)Digital Signal Processing Accelerator for RISC-V2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)10.1109/ICECS46596.2019.8964670(703-706)Online publication date: Nov-2019
https://doi.org/10.1109/ICECS46596.2019.8964670
Ge LBai YYu CZhang XWang RHuo LVozenilek V(2018)A QOS-aware dynamic resources management for data centerProceedings of the International Conference on Geoinformatics and Data Analysis10.1145/3220228.3220251(157-162)Online publication date: 20-Apr-2018
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Fu HWang PYang C(2018)Active forwardingProceedings of the 55th Annual Design Automation Conference10.1145/3195970.3195984(1-6)Online publication date: 24-Jun-2018
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Taram MVenkat ATullsen D(2018)Mobilizing the micro-opsProceedings of the 45th Annual International Symposium on Computer Architecture10.1109/ISCA.2018.00058(624-637)Online publication date: 2-Jun-2018
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Fu HWang PYang C(2018)Active Forwarding: Eliminate IOMMU Address Translation for Accelerator-rich Architectures2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)10.1109/DAC.2018.8465921(1-6)Online publication date: Jun-2018
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Kim YKim MChung S(2017)Enhancing Energy Efficiency of Multimedia Applications in Heterogeneous Mobile Multi-Core ProcessorsIEEE Transactions on Computers10.1109/TC.2017.271031766:11(1878-1889)Online publication date: 5-Oct-2017
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Majumdar APiga LPaul IGreathouse JHuang WAlbonesi D(2017)Dynamic GPGPU Power Management Using Adaptive Model Predictive Control2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)10.1109/HPCA.2017.34(613-624)Online publication date: Feb-2017
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