Predict, Share, and Recycle Your Way to Low-power Nanophotonic Networks

High static power consumption is widely regarded as one of the largest bottlenecks in creating scalable optical NoCs. The standard techniques to reduce static power are based on sharing optical channels and modulating the laser. We show in this article that state-of-the-art techniques in these areas are suboptimal, and there is a significant room for further improvement. We propose two novel techniques-a neural network-based method for laser modulation by predicting optical traffic and a distributed and altruistic algorithm for channel sharing-that are significantly closer to a theoretically ideal scheme. In spite of this, a lot of laser power still gets wasted. We propose to reuse this energy to heat micro-ring resonators (achieve thermal tuning) by efficiently recirculating it. These three methods help us significantly reduce the energy requirements. Our design consumes 4.7x lower laser power as compared to other state-of-the-art proposals. In addition, it results in a 31% improvement in performance and 39% reduction in ED2 for a suite of Splash2 and Parsec benchmarks.