@inproceedings{1f21efe450994e6c867b07fcc65a12d8,
title = "An efficient hardware architecture for multilayer spiking neural networks",
abstract = "Spiking Neural Network (SNN) is the most recent computational model that can emulate the behaviors of biological neuron system. This paper highlights and discusses an efficient hardware architecture for the hardware SNNs, which includes a layer-level tile architecture (LTA) for the neurons and synapses, and a novel routing architecture (NRA) for the interconnections between the neuron nodes. In addition, a visualization performance monitoring platform is designed, which is used as functional verification and performance monitoring for the SNN hardware system. Experimental results demonstrate that the proposed architecture is feasible and capable of scaling to large hardware multilayer SNNs.",
keywords = "FPGA, hardware architecture, spiking neural networks",
author = "Yuling Luo and Lei Wan and Junxiu Liu and Jinlei Zhang and Yi Cao",
year = "2017",
doi = "10.1007/978-3-319-70136-3_83",
language = "English",
isbn = "9783319701356",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "786--795",
editor = "Derong Liu and Shengli Xie and Dongbin Zhao and Yuanqing Li and El-Alfy, {El-Sayed M.}",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
note = "24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
}