A structure time parallel implementation of spike based deep learning
Motivated by the recent progress of deep spiking neural networks (SNNs) we propose a structure time parallel strategy based on layered structure and one time computation over a time window to speed up the prominent spike based deep learning algorithm named broadcast alignment. Furthermore, a well designed deep hierarchical model based on the parallel broadcast alignment is proposed for object recognition. The parallel broadcast alignment achieves a significant 137x speedup compared to its original implementation on MNIST dataset. The object recognition model achieves higher accuracy than that of the latest spiking deep convolutional neural networks on the ETH 80 dataset. The proposed parallel strategy and the object recognition model will facilitate both the simulation of deep SNNs for studying spiking neural dynamics and also the applications of spike based deep learning in real world problems.
Neural Networks