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 > 106× speedup with an average prediction error of 4.3% over the baseline simulator. It also achieves an average of 2× better accuracy with at least 2.3× speedup compared to state-of-the-art.