One Protocol to Rule Them All: Wireless Network-on-Chip using Deep Reinforcement Learning

Wireless Network-on-Chip (NoC) has emerged as a promising solution to scale chip multi-core processors to hundreds and thousands of cores. The broadcast nature of a wireless network allows it to significantly reduce the latency and overhead of many-to-many multicast and broadcast communication on NoC processors. Unfortunately, the traffic patterns on wireless NoCs tend to be very dynamic and can change drastically across different cores, different time intervals and different applications. New medium access protocols that can learn and adapt to the highly dynamic traffic in wireless NoCs are needed to ensure low latency and efficient network utilization.

Towards this goal, we present Neural-MCP, a unified approach that combines networking, architecture and deep learning to generate highly adaptive medium access protocols for wireless NoC architectures. Neural-MCP leverages a deep reinforcement learning framework to create new policies that can learn the structure, correlations, and statistics of the traffic patterns and adapt quickly to optimize performance. Our results show that Neural-MCP can quickly adapt to NoC traffic to provide significant gains in terms of latency, throughput, and overall execution time. In particular, for applications with highly dynamic traffic patterns, Neural-MCP can reduce the execution time by 37% - 275% as compared to 4 baselines.