Spike-based neuromorphic hardware promises to reduce the energy consumption of image classification and other deep-learning applications, particularly on mobile phones and other edge devices. However, direct training of deep spiking neural networks is difficult, and previous methods for converting trained artificial neural networks to spiking neurons were inefficient because the neurons had to emit too many spikes. We show that a substantially more efficient conversion arises when one optimizes the spiking neuron model for that purpose, so that it not only matters for information transmission how many spikes a neuron emits, but also when it emits those spikes. This advances the accuracy that can be achieved for image classification with spiking neurons, and the resulting networks need on average just two spikes per neuron for classifying an image. In addition, our new conversion method improves latency and throughput of the resulting spiking networks.

基于尖峰的神经形态硬件有望降低图像分类和其他深度学习应用程序的能耗，尤其是在手机和其他边缘设备上。然而，深度脉冲神经网络的直接训练是困难的，并且以前将训练的人工神经网络转换为脉冲神经元的方法效率低下，因为神经元必须发出太多的脉冲。我们表明，当一个人为此目的优化尖峰神经元模型时，会出现更有效的转换，因此信息传输不仅对神经元发出多少尖峰很重要，而且在它发出这些尖峰时也很重要。这提高了使用尖峰神经元进行图像分类可以实现的准确性，并且由此产生的网络平均每个神经元只需要两个尖峰来对图像进行分类。