Up by their bootstraps: Online learning in Artificial Neural Networks for CMP uncore power management

Jae Yeon Won, Xi Chen, Paul Gratz, Jiang Hu, Vassos Soteriou

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

Abstract

With increasing core counts in Chip Multi-Processor (CMP) designs, the size of the on-chip communication fabric and shared Last-Level Caches (LLC), which we term uncore here, is also growing, consuming as much as 30% of die area and a significant portion of chip power budget. In this work, we focus on improving the uncore energy-efficiency using dynamic voltage and frequency scaling. Previous approaches are mostly restricted to reactive techniques, which may respond poorly to abrupt workload and uncore utility changes. We find, however, there are predictable patterns in uncore utility which point towards the potential of a proactive approach to uncore power management. In this work, we utilize artificial intelligence principles to proactively leverage uncore utility pattern prediction via an Artificial Neural Network (ANN). ANNs, however, require training to produce accurate predictions. Architecting an efficient training mechanism without a priori knowledge of the workload is a major challenge. We propose a novel technique in which a simple Proportional Integral (PI) controller is used as a secondary classifier during ANN training, dynamically pulling the ANN up by its bootstraps to achieve accurate predictions. Both the ANN and the PI controller, then, work in tandem once the ANN training phase is complete. The advantage of using a PI controller to initially train the ANN is a dramatic acceleration of the ANN's initial learning phase. Thus, in a real system, this scenario allows quick power-control adaptation to rapid application phase changes and context switches during execution. We show that the proposed technique produces results comparable to those of pure offline training without a need for prerecorded training sets. Full system simulations using the PARSEC benchmark suite show that the bootstrapped ANN improves the energy-delay product of the uncore system by 27% versus existing state-of-the-art methodologies.

Original languageEnglish (US)
Title of host publication20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014
PublisherIEEE Computer Society
Pages308-319
Number of pages12
ISBN (Print)9781479930975
DOIs
StatePublished - Jan 1 2014
Event20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014 - Orlando, FL, United States
Duration: Feb 15 2014Feb 19 2014

Publication series

NameProceedings - International Symposium on High-Performance Computer Architecture
ISSN (Print)1530-0897

Other

Other20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014
CountryUnited States
CityOrlando, FL
Period2/15/142/19/14

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ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Won, J. Y., Chen, X., Gratz, P., Hu, J., & Soteriou, V. (2014). Up by their bootstraps: Online learning in Artificial Neural Networks for CMP uncore power management. In 20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014 (pp. 308-319). [6835941] (Proceedings - International Symposium on High-Performance Computer Architecture). IEEE Computer Society. https://doi.org/10.1109/HPCA.2014.6835941