CASIA OpenIR  > 国家专用集成电路设计工程技术研究中心
Low Latency Spiking ConvNets with Restricted Output Training and False Spike Inhibition
Chen RZ(陈睿智)1,2; Ma H(马鸿)1; Guo P(郭鹏)1; Xie SL(谢少林)1; Wang DL(王东琳)1
Conference Name2018 International Joint Conference on Neural Networks (IJCNN)
Conference Date2018-7
Conference Place巴西里约热内卢

Deep convolutional neural networks (ConvNets) have achieved the state-of-the-art performance on many real-world applications. However, significant computation and storage demands are required by ConvNets. Spiking neural networks (SNNs), with sparsely activated neurons and event-driven computations, show great potential to take advantage of the ultra-low power spike-based hardware architectures. Yet, training SNN with similar accuracy as ConvNets is difficult. Recent researchers have demonstrated the work of converting ConvNets to SNNs (CNN-SNN conversion) with similar accuracy. However, the energy-efficiency of the converted SNNs is impaired by the increased classification latency. In this paper, we focus on optimizing the classification latency of the converted SNNs. First, we propose a restricted output training method to normalize the converted weights dynamically in the CNN-SNN training phase. Second, false spikes are identified and the false spike inhibition theory is derived to speedup the convergence of the classification process. Third, we propose a temporal max pooling method to approximate the max pooling operation in ConvNets without accuracy loss. The evaluation shows that the converted SNNs converge in about 30 time-steps and achieve the best classification accuracy of 94% on CIFAR-10 dataset.

Indexed ByEI
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen RZ,Ma H,Guo P,et al. Low Latency Spiking ConvNets with Restricted Output Training and False Spike Inhibition[C],2018.
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