  This paper presents a new technique for training networks for low-precision
communication. Targeting minimal communication between nodes not only enables
the use of emerging spiking neuromorphic platforms, but may additionally
streamline processing conventionally. Low-power and embedded neuromorphic
processors potentially offer dramatic performance-per-Watt improvements over
traditional von Neumann processors, however programming these brain-inspired
platforms generally requires platform-specific expertise which limits their
applicability. To date, the majority of artificial neural networks have not
operated using discrete spike-like communication.
  We present a method for training deep spiking neural networks using an
iterative modification of the backpropagation optimization algorithm. This
method, which we call Whetstone, effectively and reliably configures a network
for a spiking hardware target with little, if any, loss in performance.
Whetstone networks use single time step binary communication and do not require
a rate code or other spike-based coding scheme, thus producing networks
comparable in timing and size to conventional ANNs, albeit with binarized
communication. We demonstrate Whetstone on a number of image classification
networks, describing how the sharpening process interacts with different
training optimizers and changes the distribution of activity within the
network. We further note that Whetstone is compatible with several
non-classification neural network applications, such as autoencoders and
semantic segmentation. Whetstone is widely extendable and currently implemented
using custom activation functions within the Keras wrapper to the popular
TensorFlow machine learning framework.
