  Deep Neural Networks (DNN) have achieved human level performance in many
image analytics tasks but DNNs are mostly deployed to GPU platforms that
consume a considerable amount of power. Brain-inspired spiking neuromorphic
chips consume low power and can be highly parallelized. However, for deploying
DNNs to energy efficient neuromorphic chips the incompatibility between
continuous neurons and synaptic weights of traditional DNNs, discrete spiking
neurons and synapses of neuromorphic chips has to be overcome. Previous work
has achieved this by training a network to learn continuous probabilities and
deployment to a neuromorphic architecture by random sampling these
probabilities. An ensemble of sampled networks is needed to approximate the
performance of the trained network.
  In the work presented in this paper, we have extended previous research by
directly learning binary synaptic crossbars. Results on MNIST show that better
performance can be achieved with a small network in one time step (92.7%
maximum observed accuracy vs 95.98% accuracy in our work). Top results on a
larger network are similar to previously published results (99.42% maximum
observed accuracy vs 99.45% accuracy in our work). More importantly, in our
work a smaller ensemble is needed to achieve similar or better accuracy than
previous work, which translates into significantly decreased energy consumption
for both networks. Results of our work are stable since they do not require
random sampling.
