CUED Publications database

A case for efficient accelerator design space exploration via Bayesian optimization

Reagen, B and Hernandez-Lobato, JM and Adolf, R and Gelbart, M and Whatmough, P and Wei, GY and Brooks, D (2017) A case for efficient accelerator design space exploration via Bayesian optimization. In: ACM/IEEE International Symposium on Low Power Electronics and Design, 2017-7-24 to 2017-7-26, Taipei, Taiwan.

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: accelerator design space exploration bayesian optimization machine learning deep neural network hardware accelerators DNN accelerators optimization space
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 24 Oct 2017 01:42
Last Modified: 15 Sep 2020 05:32
DOI: 10.1109/ISLPED.2017.8009208