Predicting Local Congestion at Fine-grain Levels in Networks-on-Chip Using Spiking Neural Networks

Research output: Contribution to conferencePaper

Abstract

Networks-on-Chip (NoC) was introduced to achieve
maximum communication performance in on-chip systems. Local
congestion caused by the queuing of data at input channel buffers
constrains NoC latency and throughput performance. NoCs
require a predictive approach to minimize the effects from local
congestion problems. In this paper we proposed a novel fine-grain
congestion prediction approach based on Spiking Neural Network
(SNN), which predicts router congestion with 30 clock cycles look-
ahead capability. Two fine-grain prediction approaches including
router and network models are proposed. The prediction
performances of the models are evaluated and analyzed using both
synthetic and real-time NoC traffic applications. Results show that
the network model is more consistent in fine-grain local congestion
prediction and requires 42% less hardware area than the router
model.
Original languageEnglish
Pages1-7
Number of pages7
Publication statusAccepted/In press - 15 Sep 2020
Event13th International Workshop on Network on Chip Architectures - Online
Duration: 18 Oct 2020 → …
http://www.nocarc.org/home

Workshop

Workshop13th International Workshop on Network on Chip Architectures
Period18/10/20 → …
Internet address

Keywords

  • Networks-on-chip
  • congestion prediction
  • network traffic
  • Spiking Neural Networks

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  • Cite this

    Javed, A., Harkin, J., McDaid, LJ., & Liu, J. (Accepted/In press). Predicting Local Congestion at Fine-grain Levels in Networks-on-Chip Using Spiking Neural Networks. 1-7. Paper presented at 13th International Workshop on Network on Chip Architectures, .