Predicting Networks-on-Chip Traffic Congestion with Spiking Neural Networks  

— Network congestion is one of the critical reasons for
degradation of data throughput performance in Networks-onChip (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 minimise its impact on
the overall NoCs throughput. The key novelty is utilising SNNs to
recognise 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 term 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.