CQ ^+ Training: Minimizing Accuracy Loss in Conversion From Convolutional Neural Networks to Spiking Neural Networks

Spiking neural networks (SNNs) are attractive for energy-constrained use-cases due to their binarized activation, eliminating the need for weight multiplication. However, its lag in accuracy compared to traditional convolutional network networks (CNNs) has limited its deployment. In this paper, we p...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2023-10, Vol.45 (10), p.11600-11611
Main Authors: Yan, Zhanglu, Zhou, Jun, Wong, Weng-Fai
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Backpropagation
Clamps
CNN-to-SNN Conversion
Conversion
Convolutional neural networks
Datasets
deep spiking neural networks
Encoding
Network latency
Neural networks
Neurons
Quantization (signal)
Training
Windows (intervals)
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