Spik4lite: Refactoring Neuromorphic Sparsity for Efficient Spiking Neural Networks on Commodity Edge Devices

Recently, the spiking neural networks (SNNs) have shown great promise in enhancing AI task performance by utilizing the brain-inspired and energy-efficient computational paradigm via the binary (0/1) spikes. Modern SNNs, especially those based on transformers, often require FPGA accelerators or neuromorphic chips to enable spike-driven computations. However, this domain-specific hardware is not always accessible on commodity edge devices like NVIDIA Jetsons, which may degrade SNNs' energy efficiency due to massive computational waste on inactive "0" spikes and finally undermine the usage boundary. This limitation raises an interesting question: is it possible to make SNNs edge-friendly and tame the computations mostly on active "1" spikes? We present the answer yes and propose Spik4lite, which serves as a lightweight plug-and-play module to significantly improve SNN's performance between model accuracy and computational efficiency. The key is to refactor SNN's channel-wise neuromorphic sparsity by zeroing out low-efficiency channels while proactively compensating for the eliminated spikes. Different from prior methods mainly focusing on optimizing the theoretical synaptic operations, our design philosophy can evolve the SNNs into a physically compact manner, thus inherently saving more computational and energy costs. Extensive experiments based on real edge devices show that Spik4lite can be integrated into existing SNN baselines to further improve their accuracy-and-efficiency performance, guaranteeing the model accuracy while saving the computational and energy costs.

Spiking neural networks (SNNs) use brain-inspired binary (0/1) spikes for energy-efficient computing. However, making them truly practical on commodity edge devices remains an important yet underexplored problem. While much of the existing methods focuses on theoretical efficiency gains, modern SNNs still rely on specialized hardware. On commodity edge devices like NVIDIA Jetsons, massive computation is wasted on inactive ''0'' spikes, leading to inefficient deployment in real-world settings. To address this critical issue, we propose Spik4lite, a lightweight plug-and-play module that makes SNNs edge-friendly. Through an energy-aware channel gating mechanism and physical channel removal, it removes low-efficiency channels while proactively compensating for the eliminated spikes to preserve accuracy. This design successfully translates theoretical sparsity into practical hardware efficiency. Experiments on NVIDIA Jetson Orin Nano 8G show that integrating Spik4lite into existing SNNs inherently saves substantial computational and energy costs while guaranteeing model accuracy, demonstrating outstanding engineering value.