  Spiking Neural Networks (SNNs) have gained significant attention as a
potentially energy-efficient alternative for standard neural networks with
their sparse binary activation. However, SNNs suffer from memory and
computation overhead due to spatio-temporal dynamics and multiple
backpropagation computations across timesteps during training. To address this
issue, we introduce Tensor Train Decomposition for Spiking Neural Networks
(TT-SNN), a method that reduces model size through trainable weight
decomposition, resulting in reduced storage, FLOPs, and latency. In addition,
we propose a parallel computation pipeline as an alternative to the typical
sequential tensor computation, which can be flexibly integrated into various
existing SNN architectures. To the best of our knowledge, this is the first of
its kind application of tensor decomposition in SNNs. We validate our method
using both static and dynamic datasets, CIFAR10/100 and N-Caltech101,
respectively. We also propose a TT-SNN-tailored training accelerator to fully
harness the parallelism in TT-SNN. Our results demonstrate substantial
reductions in parameter size (7.98X), FLOPs (9.25X), training time (17.7%), and
training energy (28.3%) during training for the N-Caltech101 dataset, with
negligible accuracy degradation.
