Self-calibrating First Spike Temporal Encoding Neuron Model
Because of the complex spatio-temporal dynamic process of spike neurons and the non-differentiable spike information,the training of spike neural network(SNN)has always been very difficult.The ANN-to-SNN method for indirect training of deep SNN avoids the difficulties of direct training of deep SNN.However,the performance of the SNN obtained in this approach is greatly affected by the spike information encoding mechanism.Among many coding mechanisms,TTFS has a good biological basis and is energy efficient,but existing TTFS codes use a single-spike formalism,which has weak information representation capabili-ty and large time windows for encoding.Therefore,based on the single spike coding of TTFS,a calibration spike is added to form a self-calibrating first spike time to first spike coding mechanism,and the corresponding SC-TTFS neuron model is constructed.In SC-TTFS,the first spike is the spike that must be emitted,while the calibration spike determines whether it is emitted according to the residual membrane potential after the first spike is emitted,which is used to compensate the quantification error and trun-cation error caused by the coding spike and to reduce the time window required for coding.The advantages of this approach are verified by comparing and analyzing the corresponding conversion errors of various codes and ANN-SNN conversion experiments on various network architectures.On CIFAR10 and CIFAR100 datasets,the proposed algorithm is verified by experiments based on VGG and ResNet network structures,and it achieves ANN-SNN transformation with non-destructive accuracy on both net-work structures and two data sets.Compared to state-of-the-art similar methods,the SNN constructed by the proposed method has the smallest network inference latency.In addition,on the VGG structure,the proposed method improves the energy efficien-cy by about 80％compared with TTFS coding.
万方数据
维普
