Online learning for adaptive optimization of heterogeneous SoCs

Ganapati Bhat, Sumit K. Mandal, Ujjwal Gupta, Umit Ogras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Energy efficiency and performance of heterogeneous multiprocessor systems-on-chip (SoC) depend critically on utilizing a diverse set of processing elements and managing their power states dynamically. Dynamic resource management techniques typically rely on power consumption and performance models to assess the impact of dynamic decisions. Despite the importance of these decisions, many existing approaches rely on fixed power and performance models learned offline. This paper presents an online learning framework to construct adaptive analytical models. We illustrate this framework for modeling GPU frame processing time, GPU power consumption and SoC power-temperature dynamics. Experiments on Intel Atom E3826, Qualcomm Snapdragon 810, and Samsung Exynos 5422 SoCs demonstrate that the proposed approach achieves less than 6% error under dynamically varying workloads.

Original languageEnglish (US)
Title of host publication2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450359504
DOIs
StatePublished - Nov 5 2018
Event37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - San Diego, United States
Duration: Nov 5 2018Nov 8 2018

Other

Other37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018
CountryUnited States
CitySan Diego
Period11/5/1811/8/18

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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    Bhat, G., Mandal, S. K., Gupta, U., & Ogras, U. (2018). Online learning for adaptive optimization of heterogeneous SoCs. In 2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers [a61] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3240765.3243489