  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.
