  Artificial Neural Network (ANN)-based inference on battery-powered devices
can be made more energy-efficient by restricting the synaptic weights to be
binary, hence eliminating the need to perform multiplications. An alternative,
emerging, approach relies on the use of Spiking Neural Networks (SNNs),
biologically inspired, dynamic, event-driven models that enhance energy
efficiency via the use of binary, sparse, activations. In this paper, an SNN
model is introduced that combines the benefits of temporally sparse binary
activations and of binary weights. Two learning rules are derived, the first
based on the combination of straight-through and surrogate gradient techniques,
and the second based on a Bayesian paradigm. Experiments validate the
performance loss with respect to full-precision implementations, and
demonstrate the advantage of the Bayesian paradigm in terms of accuracy and
calibration.
