Hotspot Prediction of Network-on-Chip for Neuromorphic Processor with Liquid State Machine

The traffic patterns of Spiking Neural Networks (SNNs) are highly varying and unpredictable, which will cause elevated traffic hotspots on the Network-on-Chip (NoC). How to predict the occurrence of hotspots remains one of the most challenging issues for NoC design. This work presents the first attempt toward utilizing a Liquid State Machine (LSM) in the prediction of traffic hotspot on routers. We also adopt the heuristic algorithm to search the hyperparameter of the LSM to improve the performance. Results indicate that the predictor can forecast hotspot formation with an accuracy up to 89.36% and 90.19% for two spiking-based datasets, respectively.