  Evaluation of spiking neural networks requires fetching a large number of
synaptic weights to update postsynaptic neurons. This limits parallelism and
becomes a bottleneck for hardware.
  We present an approach for spike propagation based on a probabilistic
interpretation of weights, thus reducing memory accesses and updates. We study
the effects of introducing randomness into the spike processing, and show on
benchmark networks that this can be done with minimal impact on the recognition
accuracy.
  We present an architecture and the trade-offs in accuracy on fully connected
and convolutional networks for the MNIST and CIFAR10 datasets on the Xilinx
Zynq platform.
