  Spiking neural networks are becoming increasingly popular for their low
energy requirement in real-world tasks with accuracy comparable to the
traditional ANNs. SNN training algorithms face the loss of gradient information
and non-differentiability due to the Heaviside function in minimizing the model
loss over model parameters. To circumvent the problem surrogate method uses a
differentiable approximation of the Heaviside in the backward pass, while the
forward pass uses the Heaviside as the spiking function. We propose to use the
zeroth order technique at the neuron level to resolve this dichotomy and use it
within the automatic differentiation tool. As a result, we establish a
theoretical connection between the proposed local zeroth-order technique and
the existing surrogate methods and vice-versa. The proposed method naturally
lends itself to energy-efficient training of SNNs on GPUs. Experimental results
with neuromorphic datasets show that such implementation requires less than 1
percent neurons to be active in the backward pass, resulting in a 100x speed-up
in the backward computation time. Our method offers better generalization
compared to the state-of-the-art energy-efficient technique while maintaining
similar efficiency.
