Fast and Accurate NoC Latency Estimation for Application-Specific Traffics via Machine Learning

Yang Li, Pingqiang Zhou

Abstract: Latency is one of the critical performance metrics for Networks-on-Chips (NoCs). When designing an NoC, the designers have to explore enormous design parameters and various traffic patterns, thus a fast and accurate latency estimator is essential to explore the large design space. In this brief, we present an ideal neural network-based methodology for latency estimation in NoCs, especially for application-specific traffics. By inputting the sequence of extracted traffic features and NoC parameters, the neural network model will infer the corresponding average network latency in a fast while accurate way. Instead of training one neural network model for each benchmark from scratch, we adopt transfer learning to train the network model for a new benchmark from another trained one. Experimental results on a set of widely used application-specific NoC benchmarks show that, our method can achieve an average estimation accuracy of 95%, and a 17.1X speedup for large NoCs compared to BookSim2 simulations. Our method can also achieve 20% to 70% improvement in accuracy over the other state-of-art machine learning-based works.