TY - GEN
T1 - A case for efficient accelerator design space exploration via Bayesian optimization
AU - Reagen, Brandon
AU - Hernandez-Lobato, Jose Miguel
AU - Adolf, Robert
AU - Gelbart, Michael
AU - Whatmough, Paul
AU - Wei, Gu Yeon
AU - Brooks, David
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/11
Y1 - 2017/8/11
N2 - In this paper we propose using machine learning to improve the design of deep neural network hardware accelerators. We show how to adapt multi-objective Bayesian optimization to overcome a challenging design problem: Optimizing deep neural network hardware accelerators for both accuracy and energy efficiency. DNN accelerators exhibit all aspects of a challenging optimization space: The landscape is rough, evaluating designs is expensive, the objectives compete with each other, and both design spaces (algorithmic and microarchitectural) are unwieldy. With multi-objective Bayesian optimization, the design space exploration is made tractable and the design points found vastly outperform traditional methods across all metrics of interest.
AB - In this paper we propose using machine learning to improve the design of deep neural network hardware accelerators. We show how to adapt multi-objective Bayesian optimization to overcome a challenging design problem: Optimizing deep neural network hardware accelerators for both accuracy and energy efficiency. DNN accelerators exhibit all aspects of a challenging optimization space: The landscape is rough, evaluating designs is expensive, the objectives compete with each other, and both design spaces (algorithmic and microarchitectural) are unwieldy. With multi-objective Bayesian optimization, the design space exploration is made tractable and the design points found vastly outperform traditional methods across all metrics of interest.
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U2 - 10.1109/ISLPED.2017.8009208
DO - 10.1109/ISLPED.2017.8009208
M3 - Conference contribution
AN - SCOPUS:85028590987
T3 - Proceedings of the International Symposium on Low Power Electronics and Design
BT - ISLPED 2017 - IEEE/ACM International Symposium on Low Power Electronics and Design
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2017
Y2 - 24 July 2017 through 26 July 2017
ER -