An NoC-Based Latency Upper-Bound Model for Reducing Timestep Length in SNN Communication

Spiking Neural Networks (SNNs) with real-time demands are deployed on Network-on-Chip (NoC)-based neuromorphic processors for specific tasks. While meeting real-time constraints for spiking data streams is crucial, overly long timesteps (e.g., 1ms) result in idle neuron cores and routers, reducing efficiency. This paper proposes a communication performance model using a recursive calculation method to assess the worst-case upper-bound latency. Integrated with the NoC router microarchitecture, the model analyzes the behavior of spiking streams during communication. It effectively balances timestep length to ensure real-time communication while minimizing idle time. Empirical results show that our model outperforms worst-contention delay (WCD) and worst-case traversal time (WCTT) models, achieving latency reductions of 0.55× to 2.53× compared to WCTT and 0.83× to 25.57× compared to WCD across various spiking datasets and virtual channels. Encouragingly, with only a 1% accuracy reduction, latency was reduced by 18%.