Method for neuromorphic implementation of convolutional neural networks

摘要

Described is a system for converting convolutional neural networks to spiking neural networks. A convolutional neural network (CNN) is adapted to fit a set of requirements of a spiking neural network (SNN), resulting in an adapted CNN. The adapted CNN is trained to obtain a set of learned weights, and the set of learned weights is then applied to a converted SNN having an architecture similar to the adapted CNN. The converted SNN is then implemented on neuromorphic hardware, resulting in reduced power consumption.