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Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators
Chen, Yu-Hsin, Joel Emer, and Vivienne Sze. 2017. “Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators.” IEEE Micro 37 (3): 12–21. https://doi.org/10.1109/MM.2017.54.
The authors demonstrate the key role dataflows play in the optimization of energy efficiency for deep neural network (DNN) accelerators. By introducing a systematic approach to analyze the problem and a new dataflow, called Row-Stationary, which is up to 2.5 times more energy efficient than existing dataflows in processing a state-of-the-art DNN, this work provides guidelines for future DNN accelerator designs.
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