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|Title:||Up by their bootstraps: Online learning in Artificial Neural Networks for CMP uncore power management||Authors:||Won, Jaeyeon
Gratz, Paul V.
|Keywords:||Computer architecture;Dynamic frequency scaling;Energy management;Forecasting;Microprocessor chips;Neural networks||Category:||Computer and Information Sciences||Field:||Natural Sciences||Issue Date:||1-Jan-2014||Publisher:||IEEE Computer Society||Source:||20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014; Orlando, FL; United States; 15 February 2014 through 19 February 2014||metadata.dc.doi:||10.1109/HPCA.2014.6835941||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/9779||ISBN:||978-147993097-5||Rights:||© 2014 IEEE.||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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