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Conference Paper
Comparison between STDP and Gradient-descent Training Processes for Spiking Neural Networks Using MNIST Digits
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- Authors
- Taewook Kang, Kwang-Il Oh, Jae-Jin Lee, Wangrok Oh
- Issue Date
- 2022-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1732-1734
- Language
- English
- Type
- Conference Paper
- Project Code
- 22HS4900, Development of ultra-low power intelligent edge SoC technology based on lightweight RISC-V processor, Koo Bon Tae
- Abstract
- Spiking neural networks (SNN) operated by the event-driven spiking process can perform rapid inferences with low power consumption, compared to other neural networks. This study presents a comparison between spike-timing-dependent plasticity (STDP)-based unsupervised training and backpropagation-based gradient-descent (BGD) training approaches for the SNNs by the evaluation of a selected MNIST subset.
- KSP Keywords
- Event-driven, Spike timing dependent plasticity, gradient descent, low power consumption, spiking neural networks, unsupervised training