Randomize and Match: Exploiting Irregular Sparsity for Energy Efficient Processing in SNNs

Abstract

Spiking Neural Networks (SNNs) have emerged as a promising alternative to traditional deep Artificial Neural Networks (ANNs) due to its power efficiency that stems from their sparse spike-based computation. However, the spike train naturally exhibits high yet unbounded sparsity. This irregularity makes hardware inefficient if deployed directly on existing sparse CNN accelerators that strictly limit the sparsity patterns. Mean-while, SNN inherently contains a large number of redundant connections among neurons, which can be further exploited to reduce the computational burden on model deployment. Therefore, exploiting sparsity is a key technique in accelerating SNN inference on edge devices.To this end, we advocate exploiting irregular sparsity in SNNs for both input spikes (dynamic) and synaptic weights (static) since sparse spikes are inherently distributed in a random pattern and irregular sparsity is more flexible than regular ones. Thus, we propose MISS, a fraMework that takes full advantage of Irregular Sparsity in the SNN through synergistic hardware and software co-design. In the software part, we employ the unstructured pruning on the synaptic weights, eliminating the redundancy in network structure to the greatest extent without affecting the model accuracy. For the hardware part, we also design a sparsity-stationary dataflow that keeps sparse weights stationary in the memory to avoid the decoding overhead. With this dataflow and the matching-based architecture, we can efficiently unify the dynamic and static irregular sparsity to support the neuron computation with a very low overhead. Extensive evaluation on a wide variety of SNNs demonstrates that MISS achieves an average of 36% (up to 57%) improvement in energy efficiency and 23% (up to 48%) speedup over the baseline SNN accelerators.