Spiking Neuron Network (SNN) uses spike sequence for data processing, so it has an excellent characteristic of low power consumption. However, due to the immaturity of learning algorithm, the multiplayer network training has difficulty in convergence. Utilizing the mature learning algorithm and fast training speed of the back-propagation network, this paper proposes a method to converse the Convolutional Neural Network (CNN) to the SNN. First, the adjustment strategy for CNN is introduced. Then after training, the weight parameters in the model are extracted, which is the corresponding synaptic weight in the layer of the SNN. Finally, a new threshold-setting algorithm based on feedback is proposed to solve the critical problem of the threshold setting of neurons in the SNN. We evaluate our method on the CIFAR-10 datasets released by Hinton’s team. The experimental results show that the image classification accuracy of the SNN is more than 98% of that of CNN, and the theoretical value of power consumption per second is 3.9[Formula: see text]mW.

中文翻译：

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获得 SNN 结构的基于转换的方法

尖峰神经元网络（SNN）采用尖峰序列进行数据处理，具有低功耗的优良特性。然而，由于学习算法的不成熟，多人网络训练存在收敛困难。利用反向传播网络成熟的学习算法和快速的训练速度，本文提出了一种将卷积神经网络（CNN）转换为SNN的方法。首先介绍CNN的调整策略。然后经过训练，提取模型中的权重参数，即SNN层中对应的突触权重。最后，针对SNN中神经元阈值设置的关键问题，提出了一种新的基于反馈的阈值设置算法。我们在 Hinton 团队发布的 CIFAR-10 数据集上评估我们的方法。