PS4: A Low Power SNN Accelerator with Spike Speculative Scheme

Zongwu Wang, Fangxin Liu, Xin Tang, Li Jiang

Abstract: Spiking neural networks (SNNs) offer computational and energy efficiency advantages over traditional artificial neural networks (ANNs) due to their event-driven representations. Unlike ANNs, which use continuous activation values, SNNs transmit information through spikes-binary events represented as either 0 or 1. This characteristic allows SNNs to replace weight multiplications in ANNs with additions, resulting in a more energy-efficient and less computationally demanding implementation. However, current SNN algorithms prioritize high accuracy and large sparsity by employing complex neuron models with sparse spike generation. This approach tends to compromise energy efficiency and increase latency. On the other hand, existing SNN hardware designs struggle to jointly exploit high parallel processing dataflows and the inherent large sparsity of (dynamic) spikes, due to the unpredictable sparsity patterns and time-dependency of these spikes. To address these issues, this paper proposes PS4, an algorithm-hardware co-design framework. PS4 exploits the inherently rich sparsity in SNN spike activity using a spatial architecture for high energy efficiency and low latency without compromising accuracy. The key insight of PS4 is selectively merging multiple time steps into single-shot computations based on output (latent) sparsity, enabling speculative fast forwarding by skipping iterative spatiotemporal computations across multiple time steps where nothing happens (i.e., output spike is zero). PS4 incorporates lightweight popcount-based circuits to efficiently handle merged time steps, maximizing spike sparsity utilization and hardware parallelism. Thus, the unexplored sparsity of output spikes can be efficiently exploited to achieve highly parallel and energy-efficient computation, with very low overheads. This enables a hardware-efficient PS4 sparsity-exploiting design, which can be efficiently integrated into existing hardware accelerators like systolic arrays. Evaluations show that PS4 outperforms the the state-of-the-art SNN accelerator PTB, PS4 achieves a significant 3.8 × performance gain without compromising energy efficiency. These results showcase the impressive performance and energy efficiency of PS4, making it a compelling choice for SNN inference tasks.