  Spiking Neural Networks (SNNs) offer a promising alternative to traditional
deep learning frameworks, since they provide higher computational efficiency
due to event-driven information processing. SNNs distribute the analog values
of pixel intensities into binary spikes over time. However, the most widely
used input coding schemes, such as Poisson based rate-coding, do not leverage
the additional temporal learning capability of SNNs effectively. Moreover,
these SNNs suffer from high inference latency which is a major bottleneck to
their deployment. To overcome this, we propose a scalable time-based encoding
scheme that utilizes the Discrete Cosine Transform (DCT) to reduce the number
of timesteps required for inference. DCT decomposes an image into a weighted
sum of sinusoidal basis images. At each time step, the Hadamard product of the
DCT coefficients and a single frequency base, taken in order, is given to an
accumulator that generates spikes upon crossing a threshold. We use the
proposed scheme to learn DCT-SNN, a low-latency deep SNN with
leaky-integrate-and-fire neurons, trained using surrogate gradient descent
based backpropagation. We achieve top-1 accuracy of 89.94%, 68.3% and 52.43% on
CIFAR-10, CIFAR-100 and TinyImageNet, respectively using VGG architectures.
Notably, DCT-SNN performs inference with 2-14X reduced latency compared to
other state-of-the-art SNNs, while achieving comparable accuracy to their
standard deep learning counterparts. The dimension of the transform allows us
to control the number of timesteps required for inference. Additionally, we can
trade-off accuracy with latency in a principled manner by dropping the highest
frequency components during inference.
