TY - GEN
T1 - Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks
AU - Choi, Sunghyun
AU - Park, Jongsun
N1 - Funding Information:
This work was supported by the Industrial Strategic Technology Development Program(10077445, Development of SoC technology based on Spiking Neural Cell for smart mobile and IoT Devices) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - Spiking neural network (SNN) is considered as one of the most promising candidates for designing neuromorphic hardware due to its low power computing capability. Since SNNs are made from imitating features of the human brain, bio-plausible spike-Timing-dependent plasticity (STDP) learning rule can be adjusted to perform unsupervised learning of SNN. In this paper, we present a spike count based early termination technique for STDP learning in SNN. To reduce redundant timesteps and calculations, spike counts of output neurons can be used to terminate the training process beforehand, thus latency and energy can be decreased. The proposed scheme reduces 50.7% of timesteps and 51.1% of total weight update during training with 0.35% accuracy drop in MNIST application.
AB - Spiking neural network (SNN) is considered as one of the most promising candidates for designing neuromorphic hardware due to its low power computing capability. Since SNNs are made from imitating features of the human brain, bio-plausible spike-Timing-dependent plasticity (STDP) learning rule can be adjusted to perform unsupervised learning of SNN. In this paper, we present a spike count based early termination technique for STDP learning in SNN. To reduce redundant timesteps and calculations, spike counts of output neurons can be used to terminate the training process beforehand, thus latency and energy can be decreased. The proposed scheme reduces 50.7% of timesteps and 51.1% of total weight update during training with 0.35% accuracy drop in MNIST application.
KW - Spiking neural network(SNN)
KW - image classification
KW - spike-Timing-dependent plasticity(STDP)
UR - http://www.scopus.com/inward/record.url?scp=85100750708&partnerID=8YFLogxK
U2 - 10.1109/ISOCC50952.2020.9333061
DO - 10.1109/ISOCC50952.2020.9333061
M3 - Conference contribution
AN - SCOPUS:85100750708
T3 - Proceedings - International SoC Design Conference, ISOCC 2020
SP - 75
EP - 76
BT - Proceedings - International SoC Design Conference, ISOCC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International System-on-Chip Design Conference, ISOCC 2020
Y2 - 21 October 2020 through 24 October 2020
ER -