Abstract
Event-driven mode of computation provides SNNs with potential to bridge the gap between excellent performance and computational load of deep neural networks. However, SNNs are difficult to train because of the discontinuity of spike signals. This paper proposes an efficient framework for CNN-to-SNN conversion, which converts pre-trained convolution neural networks (CNNs) into corresponding spiking equivalents. Different from previous work, this paper focuses on the conversion of deep CNN architectures, such as Inception and ResNet. As networks in conversion are rate-encoding, a novel weight normalization method is employed to approximate the spiking rates of SNNs to the activations of CNNs. And, inspired from homeostatic plasticity in neural system, a compensation approach is introduced to reduce the deterioration of spiking rates at deep layers and accelerate the inference of SNNs. Experimental results on CIFAR dataset show that the SNNs built by the conversion framework achieve better performance than those trained with spike-based algorithms. In particular, the accuracy gap between converted SNNs and original CNNs is further reduced, which is helpful for large-scale employment of spiking networks.
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Acknowledgments
This study was partly supported by the National Natural Science Foundation of China (No. 41571402), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 61221003), and National Key R&D Program of China (No. 2018YF-B0505000).
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Xing, F., Yuan, Y., Huo, H., Fang, T. (2019). Homeostasis-Based CNN-to-SNN Conversion of Inception and Residual Architectures. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11955. Springer, Cham. https://doi.org/10.1007/978-3-030-36718-3_15
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