TY - GEN
T1 - Spiking Neural Networks for digital hand-written number recognition
AU - Sheng, Dian
AU - Xu, Rongxuan
AU - Wang, Qinan
AU - Zhao, Chun
N1 - Funding Information:
ACKNOWLEDGMENT This research was funded in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program (19KJB510059), the Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (SYG201924), University Research Development Fund (RDF-17-01-13), and the Key Program Special Fund in XJTLU (KSF-T-03, KSF-A-07). This work was partially supported by the XJTLU AI University Research Centre and Jiangsu (Provincial) Data Science and Cognitive Computational Engineering Research Centre at XJTLU, and Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Nowadays, the advancement of deep learning has been staggering during the past decades. The state-of-art spiking neural networks (SNNs) demonstrate outstanding characteristics in accuracy, power-efficiency, and spiking timing-dependent plasticity (STDP). In view of these advantages, SNNs are a promising candidate for neural morphic application. This paper presents the performances and applications of SNNs, which are used to recognize digital hand-written numbers.
AB - Nowadays, the advancement of deep learning has been staggering during the past decades. The state-of-art spiking neural networks (SNNs) demonstrate outstanding characteristics in accuracy, power-efficiency, and spiking timing-dependent plasticity (STDP). In view of these advantages, SNNs are a promising candidate for neural morphic application. This paper presents the performances and applications of SNNs, which are used to recognize digital hand-written numbers.
KW - Leaky integrate-and-fire (LIF)
KW - MINIST
KW - Neural computing
UR - http://www.scopus.com/inward/record.url?scp=85148430971&partnerID=8YFLogxK
U2 - 10.1109/ISOCC56007.2022.10031396
DO - 10.1109/ISOCC56007.2022.10031396
M3 - Conference Proceeding
AN - SCOPUS:85148430971
T3 - Proceedings - International SoC Design Conference 2022, ISOCC 2022
SP - 185
EP - 186
BT - Proceedings - International SoC Design Conference 2022, ISOCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International System-on-Chip Design Conference, ISOCC 2022
Y2 - 19 October 2022 through 22 October 2022
ER -