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.

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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
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SRCPAR—Spike Response-Based Congestion Prediction for Adaptive Routing for 2D NoCs

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2 citations
IEEE Access
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article

SRCPAR—Spike Response-Based Congestion Prediction for Adaptive Routing for 2D NoCs

Amit Kumar Bairwa, Devershi Pallavi Bhatt, Manoj Kumar Bohra, Masoud Daneshtalab, Ashish Sharma, Sourabh Singh Verma, Rajendra Singh
article en
2 citations

Abstract

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.

IEEE AccessVol. 13
Manipal University Jaipur, Mälardalen University (SE)
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Openalex Percentile: Top 17%
Advanced Memory and Neural Computing
1.46
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