A Learning-Based Thermal-Sensitive Power Optimization Approach for Optical NoCs

Abstract

Optical networks-on-chip (NoCs) based on silicon photonics have been proposed as emerging on-chip communication architectures for chip multiprocessors with large core counts. However, due to the thermal sensitivity of optical devices used in optical NoCs, on-chip temperature variations cause significant thermal-induced optical power loss, which would counteract the power advantages of optical NoCs. To tackle this problem, in this work, we propose a learning-based thermal-sensitive power optimization approach for mesh- or torus-based optical NoCs in the presence of temperature variations. The key techniques proposed include an initial device-setting and thermal-tuning mechanism that is a device-level optimization technique, and a learning-based thermal-sensitive adaptive routing algorithm that is a network-level optimization technique. Simulation results of an 8x8 mesh-based optical NoC show that the proposed initial device-setting and thermal-tuning mechanism confines the worst-case thermal-induced optical energy consumption to be on the order of tens of pJ/bit, by avoiding significant thermal-induced optical power loss caused by temperature-dependent wavelength shifts. Besides, it shows that the learning-based thermal-sensitive adaptive routing algorithm is able to find an optimal path with the minimum estimated thermal-induced optical power consumption for each communication pair. The proposed routing has a greater space for optimization, especially for applications with more long-distance traffic.