Neuro-NoC: Energy Optimization in Heterogeneous Many-Core NoC using Neural Networks in Dark Silicon Era

Abstract

Due to the end of Dennard Scaling and the rise of dark silicon, it is essential to design energy-efficient heterogeneous NoC under critical power and thermal constraints. The challenge is to determine and configure NoC resources while meeting the application(s) requirements. Because of the large and complex many-core NoC design space (voltage/frequency scaling, link bandwidth, power-gating, etc.), design space becomes difficult to explore within a reasonable time for optimal decision at run-time. Furthermore, reactive resource management is not effective in preventing problems, such as creating thermal hotspots and exceeding power budget, from happening. Therefore, we propose a Neuro-NoC model, which utilizes neural networks learning algorithm to dynamically monitor, predict, and configure NoC resources based on online learning of the system status. Distributed cluster-wise neural network and a global neural network model for resource monitoring and configuration in many-core NoC has been proposed. Simulations demonstrate that Neuro-NoC can predict the global optimal NoC configuration with high accuracy (88%), sensitivity (97% true positive), and specificity (88% true negative).