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.
Reference: C. Stoeckl and W. Maass.
Recognizing images with at most one spike per neuron.