Machine Learning Techniques for the Prediction of NoC Core Mapping Performance
B. Naresh Kumar Reddy, Subrat Kar
Abstract
Network-on-Chips (NoCs) are suitable communication framework for on-chip multiprocessors. NoC performance parameters, such as execution time and energy consumption, affect overall processor performance. The execution time of NoC simulator mapping applications increases with the enhancement of the NoC size. To provide efficient mapping for performance improvement. In this paper, focus on an efficient mapping algorithm and applied machine learning techniques to predict the execution time and energy consumption of the mapped NoC. The experimental outcomes exhibit the proposed mapping algorithm can achieves approximately 80% and 75% accuracy for execution time and energy consumption prediction, respectively. This type of performance prediction can be constructive for ongoing processors.