Contention-aware routing for thermal-reliable optical networks-on-chip

• Main Authors: Li, Mengquan, Liu, Weichen, Duong, Luan H. K., Chen, Peng, Yang, Lei, Xiao, Chunhua
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Routing; Optical Switches
• Online Access: https://hdl.handle.net/10356/146079
• Institution: Nanyang Technological University

Optical network-on-chip (ONoC) architecture offers ultrahigh bandwidth, low latency, and low power dissipation for new-generation manycore systems. However, the benefits in communication performance and energy efficiency will be diminished by communication contention. The intrinsic thermal susceptibility is another challenge for ONoC designs. Under on-chip temperature variations, core functional devices suffer from significant thermal-induced optical power loss, which seriously threatens ONoCs’ reliability. In this article, we develop novel routing techniques to resolve both issues for ONoCs. By analyzing the thermal effect in ONoCs, we first present a routing criterion at the network level. Combined with device-level thermal tuning, it can implement thermal-reliable ONoCs. Two routing approaches, including a mixed-integer linear programming (MILP) model and a heuristic algorithm (called CAR), are further proposed to minimize communication conflicts based on guaranteed thermal reliability, and meanwhile, maximize the communication energy efficiency in the presence of on-chip thermal variations. By applying the criterion, our approaches achieve excellent performance with largely reduced complexity of design space exploration. The evaluation results based on both synthetic traffic patterns and realistic benchmarks validate the effectiveness of our approaches with an average of 126.95% improvement in communication performance and 16.12% reduction in energy overhead compared to state-of-the-art techniques. CAR only introduces 7.20% performance difference compared to the MILP model and is more scalable to large-size ONoCs.