  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.
