Classifying Images with Few Spikes per Neuron

Spiking neural networks (SNNs) promise to provide AI implementations with a drastically reduced energy budget in comparison with standard artificial neural networks (ANNs). Besides recurrent SNN modules that can be efficiently trained on-chip, many AI applications require the use of feedforward convolutional neural networks (CNNs) as preprocessors for visual or other sensory inputs. The standard solution has been to train a CNN consisting of non-spiking neurons, typically using the rectified linear ReLU function as activation function, and then to translate these CNNs with ReLU neurons via rate coding into SNNs. However this produces SNNs with long latency and small throughput, since the number of spikes that a neuron has to emit is on the order of the number N of output values of the corresponding CNN gate which subsequent layers need to be able to distinguish. We introduce a new ANN-SNN conversion - called FS-conversion - that needs only log N many time steps for that, which is optimal from the perspective of information theory. This can be achieved with a simple variation of the spiking neuron model that has no membrane leak but an exponentially decreasing firing threshold. We show that for the classification of images from ImageNet and CIFAR10 this new conversion reduces latency and drastically increases the throughput compared with rate-based conversion, while achieving almost the same classification performance as the ANN.

Recognizing Images with at most one Spike per Neuron

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.