Extending the Power-Efficiency and Performance of Photonic Interconnects for Heterogeneous Multicores with Machine Learning

Scott Van Winkle, Avinash Karanth Kodi, Razvan Bunescu, Ahmed Louri

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

8 Scopus citations

Abstract

As communication energy exceeds computation energy in future technologies, traditional on-chip electrical interconnects face fundamental challenges in the many-core era. Photonic interconnects have been proposed as a disruptive technology solution due to superior performance per Watt, distance independent energy consumption and CMOS compatibility for on-chip interconnects. Static power due to the laser being always switched on, varying link utilization due to spatial and temporal traffic fluctuations and thermal sensitivity are some of the critical challenges facing photonics interconnects. In this paper, we propose photonic interconnects for heterogeneous multicores using a checkerboard pattern that clusters CPU-GPU cores together and implements bandwidth reconfiguration using local router information without global coordination. To reduce the static power, we also propose a dynamic laser scaling technique that predicts the power level for the next epoch using the buffer occupancy of previous epoch. To further improve power-performance trade-offs, we also propose a regression-based machine learning technique for scaling the power of the photonic link. Our simulation results demonstrate a 34% performance improvement over a baseline electrical CMESH while consuming 25% less energy per bit when dynamically reallocating bandwidth. When dynamically scaling laser power, our buffer-based reactive and ML-based proactive prediction techniques show 40 - 65% in power savings with 0 - 14% in throughput loss depending on the reservation window size.

Original languageEnglish (US)
Title of host publicationProceedings - 24th IEEE International Symposium on High Performance Computer Architecture, HPCA 2018
PublisherIEEE Computer Society
Pages480-491
Number of pages12
Volume2018-February
ISBN (Electronic)9781538636596
DOIs
StatePublished - Mar 27 2018
Externally publishedYes
Event24th IEEE International Symposium on High Performance Computer Architecture, HPCA 2018 - Vienna, Austria
Duration: Feb 24 2018Feb 28 2018

Other

Other24th IEEE International Symposium on High Performance Computer Architecture, HPCA 2018
CountryAustria
CityVienna
Period2/24/182/28/18

Keywords

  • Machine Learning
  • Network on Chips
  • Photonics
  • Power Scaling

ASJC Scopus subject areas

  • Hardware and Architecture

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  • Cite this

    Van Winkle, S., Kodi, A. K., Bunescu, R., & Louri, A. (2018). Extending the Power-Efficiency and Performance of Photonic Interconnects for Heterogeneous Multicores with Machine Learning. In Proceedings - 24th IEEE International Symposium on High Performance Computer Architecture, HPCA 2018 (Vol. 2018-February, pp. 480-491). IEEE Computer Society. https://doi.org/10.1109/HPCA.2018.00048