A Support Vector Regression-Based Approach to Predict the Performance of 2D 3D On-Chip Communication Architectures

Abstract

Recently, Networks-on-Chips (NoCs) have evolved as a scalable solution to traditional bus and point-to-point architecture. NoC design performance evaluation is largely based on simulation, which is extremely slow as the architecture size increases, and it gives little insight on how distinct design parameters impact the actual performance of the network. Simulation for optimization purposes is therefore very difficult to use. In this paper, we propose a Support Vector Regression(SVR)-based framework, which can be used to analyze the performance of 2D and 3D NoC architectures. Experiments were conducted by varying architecture sizes with different virtual channels, injection rates. The framework proposed can be used to obtain fast and accurate NoC performance estimates with a prediction error 2% to 4% and minimum speedup of 3000 � to 3500�. � 2019 IEEE.