@conference {Reagen2017,
	title = {A Case for Efficient Accelerator Design Space Exploration via Bayesian Optimization},
	booktitle = {International Symposium on Low Power Electronics and Design},
	year = {2017},
	address = {Taipei, Taiwan},
	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.},
	keywords = {accelerators, bayesian, machine learning},
	url = {https://doi.org/10.1109/ISLPED.2017.8009208},
	author = {Brandon Reagen and Jose Hernandez-Lobato and Robert Adolf and Michael Gelbart and Paul Whatmough and Gu  Wei and David Brooks}
}
