Reinforcement Learning for Runtime Optimization for High Performance and Energy Efficient NoC
Md Farhadur Reza · 2021
Hundreds to thousands of cores are now being integrated on a single chip because of the transistor technology advancement. Network-on-Chip (NoC) has been the superior interconnect fabric for multi/many-core systems. As applications with unknown demands can arrive in the system and/or applications’ demands can change at runtime, it becomes essential to configure NoC dynamically for energy efficiency and high performance. However, large and complex NoC design space introduces computational and timing complexities to explore optimal trade-offs of energy-performance. Therefore, reinforcement learning (RL) techniques are proposed to predict and configure NoC resources quickly based on the learning of the system utilization and applications workloads. Multiple RL solutions with different reward signals, function approximators, and policies are discussed for runtime optimization of NoC. Results have been demonstrated to show the effectiveness of a few RL techniques.