SRCPAR—Spike Response-Based Congestion Prediction for Adaptive Routing for 2D NoCs
Network-on-Chip (NoC) architectures offer several advantages over bus-based systems, including improved scalability, efficient and high-performance interconnects, reduced wire routing congestion, and enhanced power efficiency. These benefits make NoC a promising solution for complex System-on-Chip (SoC) designs. In NoC, congestion prediction and its use in routing algorithms play a significant role in determining performance. A major challenge is the staleness of congestion information when it reaches a router. This paper introduces a Spike Response-based Congestion Prediction and Adaptive Routing (SRCPAR) algorithm to mitigate this issue. It leverages spike information alongside the local congestion data to accurately predict network congestion. Performance is evaluated using metrics such as average latency and throughput. Proposed method shows an improvement in performance for synthetic and real traffic patterns.
Authors
- Amit Kumar Bairwa (ORCID: https://orcid.org/0000-0003-1830-0661)
- Devershi Pallavi Bhatt (ORCID: https://orcid.org/0000-0001-9340-0860)
- Manoj Kumar Bohra (ORCID: https://orcid.org/0000-0002-2727-5106)
- Masoud Daneshtalab (ORCID: https://orcid.org/0000-0001-6289-1521)
- Ashish Sharma (ORCID: https://orcid.org/0000-0001-6636-4219)
- Sourabh Singh Verma
- Rajendra Singh (ORCID: https://orcid.org/0000-0003-2719-4573)
Institutions
- Manipal University Jaipur
- Mälardalen University (SE)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3583432
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46