Fast and Low-Precision Learning in GPU-Accelerated Spiking Neural Network
Xueyuan Shea, Yun Longb and Saibal Mukhopadhyayc
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
axshe6@gatech.edu
byunlong@gatech.edu
csaibal.mukhopadhyay@gatech.edu
ABSTRACT
Spiking neural network (SNN) uses biologically inspired neuron model coupled with Spike-timing-dependentplasticity (STDP) to enable unsupervised continuous learning in artificial intelligence (AI) platform. However, current SNN algorithms shows low accuracy in complex problems and are hard to operate at reduced precision. This paper demonstrates a GPU-accelerated SNN architecture that uses stochasticity in the STDP coupled with higher frequency input spike trains. The simulation results demonstrate 2 to 3 times faster learning compared to deterministic SNN architectures while maintaining high accuracy for MNIST (simple) and fashion MNIST (complex) data sets. Further, we show stochastic STDP enables learning even with 2 bits of operation, while deterministic STDP fails.
