Spiking Neural Network (SNN) is the third generation of Neural Network (NN) mimicking the natural behavior of the brain. By processing based on binary input/output, SNNs offer lower complexity, higher density and lower power consumption. This work presents an efficient software-hardware design framework for developing SNN systems in hardware. In addition, a design of low-cost neurosynaptic core is presented based on packet-switching communication approach. The evaluation results show that the ANN to SNN conversion method with the size 784:1200:1200:10 performs 99% accuracy for MNIST while the unsupervised STDP archives 89% with the size 784:400 with recurrent connections. The design of 256-neurons and 65k synapses is also implemented in ASIC 45nm technology with an area cost of 0.205 mm2.
@article{arxiv.2003.09847,
title = {An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems},
author = {Khanh N. Dang and Abderazek Ben Abdallah},
journal= {arXiv preprint arXiv:2003.09847},
year = {2020}
}