View in EDS

Predicting Networks-on-Chip traffic congestion with Spiking Neural Networks.

Saved in:
Bibliographic Details
Title: Predicting Networks-on-Chip traffic congestion with Spiking Neural Networks.
Authors: Javed, Aqib1 (AUTHOR), Harkin, Jim1 (AUTHOR), McDaid, Liam1 (AUTHOR), Liu, Junxiu1 (AUTHOR) j.liu1@ulster.ac.uk
Source: Journal of Parallel & Distributed Computing. Aug2021, Vol. 154, p82-93. 12p.
Subjects: Traffic congestion, Network routers, Forecasting, Network performance
Abstract: Network congestion is one of the critical reasons for degradation of data throughput performance in Networks-on-Chip (NoCs), with delays caused by data-buffer queuing in routers. Local buffer or router congestion impacts on network performance as it gradually spreads to neighbouring routers and beyond. In this paper, we propose a novel approach to NoC traffic prediction using Spiking Neural Networks (SNNs) and focus on predicting local router congestion so as to minimize its impact on the overall NoCs throughput. The key novelty is utilizing SNNs to recognize temporal patterns from NoC router buffers and predicting traffic hotspots. We investigate two neural models, Leaky Integrate and Fire (LIF) and Spike Response Model (SRM) to check performance in terms of prediction coverage. Results on prediction accuracy and precision are reported using a synthetic and real-time multimedia applications with simulation results of the LIF based predictor providing an average accuracy of 88.28%–96.25% and precision of 82.09%–96.73% as compared to 85.25%–95.69% accuracy and 73% and 98.48% precision performance of SRM based model when looking at congestion formations 30 clock cycles in advance of the actual hotspot occurrence. • A novel use of SNN to predict router-level congestion in NoC architectures. • We investigate two SNN models (LIF and SRM) to evaluate prediction coverage. • Results are formulated by using synthetic and real-time multimedia applications. • Proposed models provide better accuracy than existing NoC congestion predictors. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Parallel & Distributed Computing is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Network congestion is one of the critical reasons for degradation of data throughput performance in Networks-on-Chip (NoCs), with delays caused by data-buffer queuing in routers. Local buffer or router congestion impacts on network performance as it gradually spreads to neighbouring routers and beyond. In this paper, we propose a novel approach to NoC traffic prediction using Spiking Neural Networks (SNNs) and focus on predicting local router congestion so as to minimize its impact on the overall NoCs throughput. The key novelty is utilizing SNNs to recognize temporal patterns from NoC router buffers and predicting traffic hotspots. We investigate two neural models, Leaky Integrate and Fire (LIF) and Spike Response Model (SRM) to check performance in terms of prediction coverage. Results on prediction accuracy and precision are reported using a synthetic and real-time multimedia applications with simulation results of the LIF based predictor providing an average accuracy of 88.28%–96.25% and precision of 82.09%–96.73% as compared to 85.25%–95.69% accuracy and 73% and 98.48% precision performance of SRM based model when looking at congestion formations 30 clock cycles in advance of the actual hotspot occurrence. • A novel use of SNN to predict router-level congestion in NoC architectures. • We investigate two SNN models (LIF and SRM) to evaluate prediction coverage. • Results are formulated by using synthetic and real-time multimedia applications. • Proposed models provide better accuracy than existing NoC congestion predictors. [ABSTRACT FROM AUTHOR]
ISSN:07437315
DOI:10.1016/j.jpdc.2021.03.013