Fault-tolerant networks-on-chip routing with coarse and fine-grained look-ahead

Liu, J, Harkin, J, Li, Y and Maguire, L 2016, 'Fault-tolerant networks-on-chip routing with coarse and fine-grained look-ahead' , IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 35 (2) , pp. 260-273.

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Fault tolerance and adaptive capabilities are challenges for modern Networks-on-Chip (NoC) due to the increase in physical defects in advanced manufacturing processes. Two novel adaptive routing algorithms, namely coarse and fine-grained look-ahead algorithms, are proposed in this paper to enhance 2D mesh/torus NoC system fault-tolerant capabilities. These strategies use fault-flag codes from neighbouring nodes to obtain the status or conditions of real-time traffic in a NoC region; then calculate the path weights and choose the route to forward packets. This approach enables the router to minimise congestion for the adjacent connected channels and also to bypass a path with faulty channels by looking ahead at distant neighbouring router paths. The novelty of the proposed routing algorithms is the weighted path selection strategies, which make near-optimal routing decisions to maintain the NoC system performance under high fault rates. Results show that the proposed routing algorithms can achieve performance improvement compared to other state of the art works under various traffic loads and high fault rates. The routing algorithm with fine-grained look-ahead capability achieves a higher throughput compared with the coarse-grained approach under complex fault patterns. The hardware area/power overheads of both routing approaches are relatively low which does not prohibit scalability for large scale NoC implementations.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publisher: IEEE
Refereed: Yes
ISSN: 0278-0070
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:56
Last Modified: 06 Sep 2021 07:26
URI: http://usir.salford.ac.uk/id/eprint/35990

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