  Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for
implementing deep learning applications. In recent years, there have been
several proposals focused on supervised (conversion, spike-based gradient
descent) and unsupervised (spike timing dependent plasticity) training methods
to improve the accuracy of SNNs on large-scale tasks. However, each of these
methods suffer from scalability, latency and accuracy limitations. In this
paper, we propose novel algorithmic techniques of modifying the SNN
configuration with backward residual connections, stochastic softmax and hybrid
artificial-and-spiking neuronal activations to improve the learning ability of
the training methodologies to yield competitive accuracy, while, yielding large
efficiency gains over their artificial counterparts. Note, artificial
counterparts refer to conventional deep learning/artificial neural networks.
Our techniques apply to VGG/Residual architectures, and are compatible with all
forms of training methodologies. Our analysis reveals that the proposed
solutions yield near state-of-the-art accuracy with significant
energy-efficiency and reduced parameter overhead translating to hardware
improvements on complex visual recognition tasks, such as, CIFAR10, Imagenet
datatsets.
