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.

网络拥塞是片上网络（NoC）中​​数据吞吐性能下降的关键原因之一，而延迟是由路由器中的数据缓冲区排队引起的。随着本地缓冲区或路由器的拥塞逐渐蔓延到邻近的路由器以及其他路由器，它们会影响网络性能。在本文中，我们提出了一种使用尖峰神经网络（SNN）进行NoC流量预测的新颖方法，并着重于预测本地路由器拥塞，从而最大程度地降低其对总体NoC吞吐量的影响。关键新颖之处在于利用SNN从NoC路由器缓冲区识别时间模式并预测流量热点。我们调查了两个神经网络模型，即泄漏集成和火灾（LIF）和峰值响应模型（SRM），以检查预测覆盖率方面的性能。