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Investigation Of Lstm Based Prediction For Dynamic Energy Management In Chip Multiprocessors, Milad Ghorbani Moghaddam, Wenkai Guan, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption …