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
PublisherIEEE
Pages158-163
Number of pages6
ISBN (Electronic)978-1-7281-3391-1
ISBN (Print)978-1-7281-3392-8
DOIs
Publication statusPublished - 1 Jul 2019
Event18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 - Miami, United States
Duration: 15 Jul 201917 Jul 2019

Conference

Conference18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
CountryUnited States
CityMiami
Period15/07/1917/07/19

    Research areas

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

ID: 62407193