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|Title:||Contention-aware routing for thermal-reliable optical networks-on-chip||Authors:||Li, Mengquan
Duong, Luan H. K.
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2020||Source:||Li, M., Liu, W., Duong, L. H. K., Chen, P., Yang, L., & Xiao, C. (2021). Contention-aware routing for thermal-reliable optical networks-on-chip. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 40(2), 260-273. doi:10.1109/TCAD.2020.2994261||Journal:||IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems||Abstract:||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.||URI:||https://hdl.handle.net/10356/146079||ISSN:||0278-0070||DOI:||10.1109/TCAD.2020.2994261||Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCAD.2020.2994261||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on Jun 15, 2021
Updated on Jun 15, 2021
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