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Quantized STDP-based online-learning spiking neural network

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Abstract

In this work, we report a spike-timing-dependent plasticity (STDP)-based weight-quantized/binarized online-learning spiking neural network (SNN). The SNN uses bio-plausible integrate-and-fire (IF) neuron and conductance-based synapse as the basic building blocks and realizes online learning by STDP and winner-take-all (WTA) mechanism. Weight quantization/binarization is introduced into the online-learning SNN to reduce storage requirements and improve computing efficiency. After the training process with STDP and weight quantization on the MNIST training set, the quantized SNN with 4-bit weight achieves a recognition accuracy of 93.8% on the MNIST test set, showing little loss compared with the accuracy of the non-quantized 32-bit SNN (94.1%). The accuracy of the binarized SNN slightly decreases to 92.9%, which is cost-effective considering the reduction in the weight storage space by ~ 32 times, and the product of input and weight in the binarized SNN can be realized by computationally cheap 1-bit “AND” operation. The proposed weight quantization/binarization online-learning scheme can largely save hardware costs. The area of the quantized (8-bit and 4-bit) and binarized (1-bit) SNN-based hardware is evaluated to be 448,524, 179,263, and 162,129 μm2, respectively, which is much smaller than their non-quantized 32-bit competitor (area of ~ 5.862 × 108 μm2). The hardware resource evaluation also provides a guide to make a trade-off between computational cost and performance. Moreover, the quantized/binarized STDP training method can be further extended to train various types of SNNs. In this regard, a hybrid STDP SNN and a hybrid STDP convolutional SNN, which are trained by combining unsupervised quantized/binarized STDP and supervised backpropagation (BP) training methods, achieve high accuracy in facial expression recognition scenarios.

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Acknowledgements

This work was supported in part by NSFC under Projects 61771097 and 61774028 and in part by the Fundamental Research Funds for the Central Universities under Project ZYGX2016Z007.

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Authors and Affiliations

  1. Brain-Inspired Integrated Chip and Systems Research Center, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    S. G. Hu & Y. Liu

  2. State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    S. G. Hu, G. C. Qiao, Q. Yu, Y. Liu & L. M. Rong

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    T. P. Chen

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  1. S. G. Hu
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  2. G. C. Qiao
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Correspondence to Q. Yu.

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Hu, S.G., Qiao, G.C., Chen, T.P. et al. Quantized STDP-based online-learning spiking neural network. Neural Comput & Applic 33, 12317–12332 (2021). https://doi.org/10.1007/s00521-021-05832-y

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  • DOI: https://doi.org/10.1007/s00521-021-05832-y

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