Exploring Spiking Neural Networks for Prediction of Traffic Congestion in Networks-on-Chip

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Abstract

Networks-on-Chip (NoC) is the most modular and
scalable solution for next generation hardware communication
where significant data traffic loads are shared across many
communication paths. One key challenge in maximising NoC
performance is traffic congestion. The management of congestion
at the earliest stage can significantly minimize the impact on NoC
throughput. Prediction of NoC congestion offers a pre-emptive
strategy in maximising NoC throughput. This paper proposes a
novel spiking neural network (SNN) approach to prediction of
traffic congestion. The proposed SNN exploits the temporal nature
of the traffic to identify congestion patterns. The proposed SNN
explores two models and both are trained and evaluated to predict
local congestion 30 clock cycles in advance of occurring. Results
shows that the SNN predictor utilizes 9 times less hardware area
than previous approaches and can achieved up to 96.59% in
accuracy.
I
Original languageEnglish
Pages1-5
Number of pages5
Publication statusAccepted/In press - 5 Jan 2020
EventIEEE International Symposium on Circuits and Systems 2020: ISCAS2020 - Barceló Renacimiento, Seville, Spain
Duration: 17 May 202020 May 2020
https://iscas2020.org/

Conference

ConferenceIEEE International Symposium on Circuits and Systems 2020
Abbreviated titleISCAS2020
CountrySpain
CitySeville
Period17/05/2020/05/20
Internet address

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  • Cite this

    Javed, A., Harkin, J., McDaid, LJ., & Liu, J. (Accepted/In press). Exploring Spiking Neural Networks for Prediction of Traffic Congestion in Networks-on-Chip. 1-5. Paper presented at IEEE International Symposium on Circuits and Systems 2020, Seville, Spain.