论文标题
识别每个神经元最多具有一个尖峰的图像
Recognizing Images with at most one Spike per Neuron
论文作者
论文摘要
为了将训练有素的人工神经网络（ANN）的性能移植到尖峰神经网络（SNN），可以在神经形态硬件中实施，并大大降低能量消耗，需要有效的ANN转换。先前的转换方案集中在ANN中通过尖峰神经元的发射速率在ANN中的模拟输出的表示。但这对于其他常用的Ann门是不可能的，即使对于Relu门，它也可以减少吞吐量。我们介绍了一种新的转换方法，其中ANN中的一个栅极基本上可以是任何类型的，它是由一小部分尖峰神经元所模仿的，每个神经元最多只有一个尖峰（AMOS）。我们表明，这种AMOS转换将Imagenet的SNN的准确性从74.60％提高到80.97％，从而使其达到了最佳可用ANN准确性（85.0％）。 SNN的TOP5精度提高到95.82％，接近ANN的最佳TOP5表现。此外，AMOS转换通过几个数量级来改善基于尖峰的图像分类的延迟和吞吐量。因此，这些结果表明，SNN提供了一个可行的方向，用于为AI提供高能效率的硬件，该硬件将高性能与应用程序的多功能性相结合。
In order to port the performance of trained artificial neural networks (ANNs) to spiking neural networks (SNNs), which can be implemented in neuromorphic hardware with a drastically reduced energy consumption, an efficient ANN to SNN conversion is needed. Previous conversion schemes focused on the representation of the analog output of a rectified linear (ReLU) gate in the ANN by the firing rate of a spiking neuron. But this is not possible for other commonly used ANN gates, and it reduces the throughput even for ReLU gates. We introduce a new conversion method where a gate in the ANN, which can basically be of any type, is emulated by a small circuit of spiking neurons, with At Most One Spike (AMOS) per neuron. We show that this AMOS conversion improves the accuracy of SNNs for ImageNet from 74.60% to 80.97%, thereby bringing it within reach of the best available ANN accuracy (85.0%). The Top5 accuracy of SNNs is raised to 95.82%, getting even closer to the best Top5 performance of 97.2% for ANNs. In addition, AMOS conversion improves latency and throughput of spike-based image classification by several orders of magnitude. Hence these results suggest that SNNs provide a viable direction for developing highly energy efficient hardware for AI that combines high performance with versatility of applications.
