  Spiking Neural Networks (SNNs) with a large number of weights and varied
weight distribution can be difficult to implement in emerging in-memory
computing hardware due to the limitations on crossbar size (implementing dot
product), the constrained number of conductance levels in non-CMOS devices and
the power budget. We present a sparse SNN topology where non-critical
connections are pruned to reduce the network size and the remaining critical
synapses are weight quantized to accommodate for limited conductance levels.
Pruning is based on the power law weight-dependent Spike Timing Dependent
Plasticity (STDP) model; synapses between pre- and post-neuron with high spike
correlation are retained, whereas synapses with low correlation or uncorrelated
spiking activity are pruned. The weights of the retained connections are
quantized to the available number of conductance levels. The process of pruning
non-critical connections and quantizing the weights of critical synapses is
performed at regular intervals during training. We evaluated our sparse and
quantized network on MNIST dataset and on a subset of images from Caltech-101
dataset. The compressed topology achieved a classification accuracy of 90.1%
(91.6%) on the MNIST (Caltech-101) dataset with 3.1x (2.2x) and 4x (2.6x)
improvement in energy and area, respectively. The compressed topology is energy
and area efficient while maintaining the same classification accuracy of a
2-layer fully connected SNN topology.
