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)
 | There is a more recent version of this item available. |
![[img]](https://eprints.uet.vnu.edu.vn/eprints/style/images/fileicons/application_pdf.png) |
PDF
Restricted to Repository staff only Download (915kB) |
Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Electronics and Communications |
| Divisions: | Key Laboratory for Smart Integrated Systems (SISLAB) |
| Depositing User: | Khanh N. Dang |
| Date Deposited: | 06 Dec 2019 07:51 |
| Last Modified: | 06 Dec 2019 07:51 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/3675 |
Available Versions of this Item
- An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems. (deposited 06 Dec 2019 07:51) [Currently Displayed]
Actions (login required)
 |
View Item |
