SVR-NoC: a performance analysis tool for network-on-chips using learning-based support vector regression model

Zhiliang Qian, Da-Cheng Juan, Paul Bogdan, Chi-Ying Tsui, Diana Marculescu, Radu Marculescu

Abstract: In this work, we propose SVR-NoC, a learning-based support vector regression (SVR) model for evaluating Network-on-Chip (NoC) latency performance. Different from the state-of-the-art NoC analytical model, which uses classical queuing theory to directly compute the average channel waiting time, the proposed SVR-NoC model performs NoC latency analysis based on learning the typical training data. More specifically, we develop a systematic machine-learning framework that uses the kernel-based support vector regression method to predict the channel average waiting time and the traffic flow latency. Experimental results show that SVR-NoC can predict the average packet latency accurately while achieving about 120X speed-up over simulation-based evaluation methods.