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Workload prediction for adaptive power scaling using deep learning
2014 IEEE International Conference on IC Design & Technology
We apply hierarchical sparse coding, a form of deep learning, to model user-driven workloads based on on-chip hardware performance counters. We then predict periods of low instruction throughput, during which frequency and voltage can be scaled to reclaim power. Using a multi-layer coding structure, our method progressively codes counter values in terms of a few prominent features learned from data, and passes them to a Support Vector Machine (SVM) classifier where they act as signatures fordoi:10.1109/icicdt.2014.6838580 dblp:conf/icicdt/TarsaKK14 fatcat:3mgqfif45jc5ldklm5sk7bjvzm