Energy-Aware DNN Graph Optimization

Yu Wang; Rong Ge; Shuang Qiu

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arXiv · 2005.05837

Energy-Aware DNN Graph Optimization

Yu Wang·Rong Ge·Shuang Qiu

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Abstract

Unlike existing work in deep neural network (DNN) graphs optimization for inference performance, we explore DNN graph optimization for energy awareness and savings for power- and resource-constrained machine learning devices. We present a method that allows users to optimize energy consumption or balance between energy and inference performance for DNN graphs. This method efficiently searches through the space of equivalent graphs, and identifies a graph and the corresponding algorithms that incur the least cost in execution. We implement the method and evaluate it with multiple DNN models on a GPU-based machine. Results show that our method achieves significant energy savings, i.e., 24% with negligible performance impact.

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Yu Wang, Rong Ge, Shuang Qiu. 2020-05-12. Energy-Aware DNN Graph Optimization. https://arxiv.org/abs/2005.05837

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