Reinforcement Learning (RL)-Based Holistic Routing and Wavelength Assignment in Optical Network-on-Chip (ONoC): Distributed or Centralized?

Abstract

With the development of silicon photonic interconnects, Optical Network-on-Chip (ONoC) becomes promising for multi-core/many-core communication. In ONoCs, both routing and wavelength assignment have an impact on the communication reliability and performance. However, the interactive impact of the routing and wavelength assignment is rarely considered. To fill this gap, this work proposes an adaptive and holistic method of routing and wavelength assignment (RWA) based on Reinforcement Learning (RL) for ONoCs. Routing and wavelength assignment is treated as a whole problem and participate in the same Markov decision process. Two corresponding implementation methods, i.e., distributed and centralized, are proposed, by using intelligent learning algorithms to process and learn the dynamic on-chip network information in multi-dimensional. Instead of considering routing and wavelength assignment separately in steps, the evaluation results show that the proposed holistic method improves by 2.58 dB, 9.21%, and 53.26% in the aspects of OSNR, waiting delay, and wavelength utilization respectively, in cost of 16.15% loss of load balancing. As for the distributed method and centralized method, the distributed method improves by 0.37 dB and 0.69% in the aspects of OSNR and waiting delay, but the centralized method improves by 13.84% and 4.46% in the aspects of load balancing and wavelength utilization.