relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3675/
title: An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems
creator: Dang, Nam Khanh
creator: Abdallah, Abderazek Ben
subject: Electronics and Communications
description: 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.
date: 2019
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3675/1/IINTEC19_Tun_CRP.pdf
identifier:   Dang, Nam Khanh and Abdallah, Abderazek Ben  (2019) An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems.  In: The International Conference on Internet of Things, Embedded Systems and Communications (IINTEC 2019.    (In Press)