  Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal
computing paradigm for a wide range of low-power vision tasks. However,
state-of-the-art (SOTA) SNN models either incur multiple time steps which
hinder their deployment in real-time use cases or increase the training
complexity significantly. To mitigate this concern, we present a training
framework (from scratch) for one-time-step SNNs that uses a novel variant of
the recently proposed Hoyer regularizer. We estimate the threshold of each SNN
layer as the Hoyer extremum of a clipped version of its activation map, where
the clipping threshold is trained using gradient descent with our Hoyer
regularizer. This approach not only downscales the value of the trainable
threshold, thereby emitting a large number of spikes for weight update with a
limited number of iterations (due to only one time step) but also shifts the
membrane potential values away from the threshold, thereby mitigating the
effect of noise that can degrade the SNN accuracy. Our approach outperforms
existing spiking, binary, and adder neural networks in terms of the
accuracy-FLOPs trade-off for complex image recognition tasks. Downstream
experiments on object detection also demonstrate the efficacy of our approach.
