Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability

Anderson Luiz Sartor, Pedro Henrique Exenberger Becker, Stephan Wong, Radu Marculescu, Antonio Carlos Schneider Beck

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


Adaptive processors can dynamically change their hardware configuration by tuning several knobs that optimize a given metric, according to the current application. However, the complexity of choosing the best setup at runtime increases exponentially as more adaptive resources become available. Therefore, we propose a polymorphic VLIW processor coupled to a machine learning-based decision mechanism that quickly and accurately delivers the best trade-off in terms of energy, performance, and reliability. The proposed system predicts the best processor configuration in 97.37% of the test cases and achieves an efficiency that is close to an oracle (more than 93.30% on all benchmarks).

Original languageEnglish
Title of host publication2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
Subtitle of host publicationProceedings
EditorsL. O'Conner
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)978-1-7281-3391-1
ISBN (Print)978-1-7281-3392-8
Publication statusPublished - 1 Jul 2019
Event18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 - Miami, United States
Duration: 15 Jul 201917 Jul 2019


Conference18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
Country/TerritoryUnited States


  • Adaptive processor
  • Energy consumption
  • Fault tolerance
  • Machine learning
  • Runtime optimization

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