Low-overhead Aging-Aware Resource Management on Embedded GPUs

Haeseung Lee, Muhammad Shafique, Mohammad Abdullah Al Faruque

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


GPUs have been employed in the embedded systems to handle increased amount of computation and satisfy the timing requirement. Therefore, the lifetime of embedded GPUs is considered one of the most important aspects to ensure functional correctness over a long period of time. Moreover, existing state-of-The-Art compiler-based GPU aging management techniques suffer from a considerable amount of performance overhead. In this paper, we propose a low-overhead aging-Aware resource management technique. The proposed technique extends the behavior of the existing warp scheduler and the instruction dispatcher to cluster the computational cores and distribute instructions based on the aging information. Compared to when using the original applications, our technique improves the aging of the embedded GPU by 30% on average. In addition, compared to the state-of-The-Art GPU aging management technique, our technique reduces the performance overhead by 16.4% on average while improving the aging by 3% on average.

Original languageEnglish (US)
Title of host publicationProceedings of the 54th Annual Design Automation Conference 2017, DAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450349277
StatePublished - Jun 18 2017
Event54th Annual Design Automation Conference, DAC 2017 - Austin, United States
Duration: Jun 18 2017Jun 22 2017

Publication series

NameProceedings - Design Automation Conference
VolumePart 128280
ISSN (Print)0738-100X


Other54th Annual Design Automation Conference, DAC 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


Dive into the research topics of 'Low-overhead Aging-Aware Resource Management on Embedded GPUs'. Together they form a unique fingerprint.

Cite this