Dynamic machine learning based matching of nonvolatile processor microarchitecture to harvested energy profile

Kaisheng Ma, Xueqing Li, Yongpan Liu, John Sampson, Yuan Xie, Vijaykrishnan Narayanan

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

19 Scopus citations

Abstract

Energy harvesting systems without an energy storage device have to efficiently harness the fluctuating and weak power sources to ensure the maximum computational progress. While a simpler processor enables a higher turn-on potential with a weak source, a more powerful processor can utilize more energy that is harvested. Earlier work shows that different complexity levels of nonvolatile microarchitectures provide best fit for different power sources, and even different trails within same power source. In this work, we propose a dynamic nonvolatile microarchitecture by integrating all non-pipelined (NP), N-stage-pipeline (NSP), and Out of Order (OoO) cores together. Neural network machine learning algorithms are also integrated to dynamically adjust the microarchitecture to achieve the maximum forward progress. This integrated solution can achieve forward progress equal to 2.4× of the baseline NP architecture (1.82× of an OoO core).

Original languageEnglish (US)
Title of host publication2015 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages670-675
Number of pages6
ISBN (Electronic)9781467383882
DOIs
StatePublished - Jan 5 2016
Event34th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2015 - Austin, United States
Duration: Nov 2 2015Nov 6 2015

Publication series

Name2015 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2015

Other

Other34th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2015
Country/TerritoryUnited States
CityAustin
Period11/2/1511/6/15

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design

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