EAST-DNN: Expediting architectural SimulaTions using deep neural networks
Arko Dutt, Govind Narasimman, Jie Lin, Vijay Chandrasekhar, Mohamed M. Sabry · 2019
A rapid and accurate architectural simulator is a cornerstone for an efficient design-space exploration of computing systems. In this paper, we introduce EAST-DNN, a feed-forward deep neural network, to accelerate architectural simulations. EAST-DNN achieves > 106X speedup with an average prediction error of 4.3% over the baseline simulator. It also achieves an average of 2X better accuracy with at least 2.3X speedup compared to state-of-the-art.