  Spike-based neuromorphic hardware promises to reduce the energy consumption
of image classification and other deep learning applications, particularly on
mobile phones or 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.
