Routing in optical network-on-chip : minimizing contention with guaranteed thermal reliability

Communication contention and thermal susceptibility are two potential issues in optical network-on-chip (ONoC) architecture, which are both critical for ONoC designs. However, minimizing conflict and guaranteeing thermal reliability are incompatible in most cases. In this paper, we present a routing...

Full description

Saved in:
Bibliographic Details
Main Authors: Li, Mengquan, Liu, Weichen, Yang, Lei, Chen, Peng, Liu, Duo, Guan, Nan
Other Authors: School of Computer Science and Engineering
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/145285
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Communication contention and thermal susceptibility are two potential issues in optical network-on-chip (ONoC) architecture, which are both critical for ONoC designs. However, minimizing conflict and guaranteeing thermal reliability are incompatible in most cases. In this paper, we present a routing criterion in the network level. Combined with device-level thermal tuning, it can implement thermal-reliable ONoC. We further propose two routing approaches (including a mixed-integer linear programming (MILP) model and a heuristic algorithm (CAR)) to minimize communication conflict based on the guaranteed thermal reliability, and meanwhile, mitigate the energy overheads of thermal regulation in the presence of chip thermal variations. By applying the criterion, our approaches achieve excellent performance with largely reduced complexity of design space exploration. Evaluation results on synthetic communication traces and realistic benchmarks show that the MILP-based approach achieves an average of 112.73% improvement in communication performance and 4.18% reduction in energy overhead compared to state-of-the-art techniques. Our heuristic algorithm only introduces 4.40% performance difference compared to the optimal results and is more scalable to large-size ONoCs.