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Decoupling GPGPU voltage-frequency scaling for deep-learning applications

  • Published:2022-07 Issue: Volume:165 Page:32-51
  • ISSN:0743-7315
  • Container-title:Journal of Parallel and Distributed Computing
  • language:en
  • Short-container-title:Journal of Parallel and Distributed Computing

Author:

Mendes Francisco,Tomás Pedro,Roma NunoORCID

Funder

Fundação para a Ciência e a Tecnologia

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference26 articles.

1. Predictable GPUs frequency scaling for energy and performance;Fan,2019

2. Accurate energy and performance prediction for frequency-scaled GPU kernels;Fan;Computation,2020

3. Modeling the effect of process, power-supply voltage and temperature variations on the timing response of nanometer digital circuits;Freijedo;J. Electron. Test.,2012

4. GPGPU power modeling for multi-domain voltage-frequency scaling;Guerreiro,2018

5. GPU static modeling using PTX and deep structured learning;Guerreiro;IEEE Access,2019

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1. Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance Assurance;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22

2. FROST: Towards Energy-efficient AI-on-5G Platforms – A GPU Power Capping Evaluation;2023 IEEE Conference on Standards for Communications and Networking (CSCN);2023-11-06

3. Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode;Journal of Database Management;2023-02-24
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