  Bio-inspired spiking neural networks (SNNs), operating with asynchronous
binary signals (or spikes) distributed over time, can potentially lead to
greater computational efficiency on event-driven hardware. The state-of-the-art
SNNs suffer from high inference latency, resulting from inefficient input
encoding, and sub-optimal settings of the neuron parameters (firing threshold,
and membrane leak). We propose DIET-SNN, a low-latency deep spiking network
that is trained with gradient descent to optimize the membrane leak and the
firing threshold along with other network parameters (weights). The membrane
leak and threshold for each layer of the SNN are optimized with end-to-end
backpropagation to achieve competitive accuracy at reduced latency. The analog
pixel values of an image are directly applied to the input layer of DIET-SNN
without the need to convert to spike-train. The first convolutional layer is
trained to convert inputs into spikes where leaky-integrate-and-fire (LIF)
neurons integrate the weighted inputs and generate an output spike when the
membrane potential crosses the trained firing threshold. The trained membrane
leak controls the flow of input information and attenuates irrelevant inputs to
increase the activation sparsity in the convolutional and dense layers of the
network. The reduced latency combined with high activation sparsity provides
large improvements in computational efficiency. We evaluate DIET-SNN on image
classification tasks from CIFAR and ImageNet datasets on VGG and ResNet
architectures. We achieve top-1 accuracy of 69% with 5 timesteps (inference
latency) on the ImageNet dataset with 12x less compute energy than an
equivalent standard ANN. Additionally, DIET-SNN performs 20-500x faster
inference compared to other state-of-the-art SNN models.
