Journal article 392 views
Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing
Junxiu Liu,
Yanhu Wang,
Yuling Luo 
,
Shunsheng Zhang,
Dong Jiang,
Yifan Hua,
Sheng Qin,
Scott Yang
,
Shunsheng Zhang,
Dong Jiang,
Yifan Hua,
Sheng Qin,
Scott Yang
Neural Processing Letters
Swansea University Author:
Scott Yang
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1007/s11063-023-11255-8
Abstract
Spiking Neural Networks (SNNs) can closely mimic the biological neural network systems. Recently, the SNNs have been developed in hardware circuits to emulate the time encoding and information-processing aspects of the human brain in real-time. However, the hardware SNN systems are suffering from la...
| Published in: | Neural Processing Letters |
|---|---|
| ISSN: | 1370-4621 1573-773X |
| Published: |
Springer Science and Business Media LLC
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa63218 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
Spiking Neural Networks (SNNs) can closely mimic the biological neural network systems. Recently, the SNNs have been developed in hardware circuits to emulate the time encoding and information-processing aspects of the human brain in real-time. However, the hardware SNN systems are suffering from large hardware resource consumption due to the high complexity of computational units. In this paper, a novel hardware SNN system based on stochastic computing is proposed to address this problem. Pair-based spiking-timing-dependent plasticity, coupled with integrate-and-fire neurons are employed to design the SNN. Stochastic computing can simplify the computational components of multipliers, adders, and subtractors in conventional hardware SNNs, hence reduce the hardware resource cost. Experimental results show that compared with the state-of-the-art approaches the proposed SNN system reduces the resource consumption by 58.0% (especially registers by ≥ 65.6%). In the meantime, the maximum normalized root mean square error between the proposed hardware and others is only 0.0097, which can maintain the behaviours of SNN. This work provides a beneficial alternative to the large-scale hardware SNN implementations. |
|---|---|
| Keywords: |
Spiking neural networks, Pair-based spiking-timing-dependent plasticity, integrate-and-fire neurons, stochastic computing |
| College: |
Faculty of Science and Engineering |
| Funders: |
This research is supported by the National Natural Science Foundation of China under Grant 61976063, the Guangxi Natural Science Foundation under Grant 2022GXNSFFA035028, research fund of Guangxi Normal University under Grant 2021JC006, the AI+Education research project of Guangxi Humanities Society Science Development Research Center under Grant ZXZJ202205. |