Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators
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_2017__Micro_Top_Picks__Eyeriss__Article_.pdf
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Author(s)
Chen, Yu-Hsin
Emer, Joel S
Sze, Vivienne
Date Issued
June 2017
Journal
IEEE Micro
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chen, Yu-Hsin et al. "Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators." IEEE Micro 37, 3 (June 2017): 12 - 21. © 2017 IEEE
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Microsystems Technology Laboratories
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/mm.2017.54
