  Spiking neural networks (SNNs) promise ultra-low-power applications by
exploiting temporal and spatial sparsity. The number of binary activations,
called spikes, is proportional to the power consumed when executed on
neuromorphic hardware. Training such SNNs using backpropagation through time
for vision tasks that rely mainly on spatial features is computationally
costly. Training a stateless artificial neural network (ANN) to then convert
the weights to an SNN is a straightforward alternative when it comes to image
recognition datasets. Most conversion methods rely on rate coding in the SNN to
represent ANN activation, which uses enormous amounts of spikes and, therefore,
energy to encode information. Recently, temporal conversion methods have shown
promising results requiring significantly fewer spikes per neuron, but
sometimes complex neuron models. We propose a temporal ANN-to-SNN conversion
method, which we call Quartz, that is based on the time to first spike (TTFS).
Quartz achieves high classification accuracy and can be easily implemented on
neuromorphic hardware while using the least amount of synaptic operations and
memory accesses. It incurs a cost of two additional synapses per neuron
compared to previous temporal conversion methods, which are readily available
on neuromorphic hardware. We benchmark Quartz on MNIST, CIFAR10, and ImageNet
in simulation to show the benefits of our method and follow up with an
implementation on Loihi, a neuromorphic chip by Intel. We provide evidence that
temporal coding has advantages in terms of power consumption, throughput, and
latency for similar classification accuracy. Our code and models are publicly
available.
