Work-in-Progress: EAST-DNN: Expediting Architectural SimulaTions Using Deep Neural Networks

Abstract

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 $> 10^{6}\times$ speedup with an average prediction error of 4.3% over the baseline simulator. It also achieves an average of $2\times$ better accuracy with at least $2.3\times$ speedup compared to state-of-the-art.