SNN Simulation Performance Prediction: A Nonempirical Method

As a third generation artificial neural network, spiking neuron network is expected to expand the artificial intelligence world. However, as a more detailed simulation of brain, a single run of spiking neural networks (SNNs) simulation can take hours to days. To get a better prediction of SNN simulation performance, existing work requires gathering result of actual runs to conduct accurate modeling. In this paper, we propose a nonempirical SNN simulation performance prediction method, prototyped in a hybrid CPU-FPGA cluster. Experiments show that our method, without actual simulation run, can get comparable accuracy with orders of magnitude less runtime cost.

Automated summary

This paper proposes a nonempirical method for predicting the performance of spiking neural network (SNN) simulations, implemented in a hybrid CPU-FPGA cluster. Experimental results show that the method can achieve comparable accuracy without actual simulation runs, with significantly reduced runtime cost.