RLE Recent Papers

Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators

Yu-Hsin Chen, Joel Emer, Vivienne Sze

DOI: 10.1109/MM.2017.54

Abstract:

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.