Millimeter wave wireless network on chip using deep reinforcement learning

Abstract

Wireless Network-on-Chip (NoC) has emerged as a promising solution to scale chip multi-core processors to hundreds of cores. However, traditional medium access protocols fall short here since the traffic patterns on wireless NoCs tend to be very dynamic and can change drastically across different cores, different time intervals and different applications. In this work, we present NeuMAC, a unified approach that combines networking, architecture and AI to generate highly adaptive medium access protocols that can learn and optimize for the structure, correlations and statistics of the traffic patterns on the NoC. Our results show that NeuMAC can quickly adapt to NoC traffic to provide significant gains in terms of latency and overall execution time, improving the execution time by up to 1.69X - 3.74X.