Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standard leaky integrate-and-fire (LIF) neurons communicate only through binary spikes, which severely limit representational capacity. Existing multi-level spiking neurons improve information transmission, but often rely on uniform quantization that mismatches membrane-potential distributions or introduces costly synaptic multiplications. In this paper, we propose ShiftLIF, a multi-level spiking neuron that maps membrane potentials to a logarithmically spaced power-of-two spike set. This design provides finer representation in the small-amplitude regime, where membrane potentials are densely concentrated, while enabling multiplier-free synaptic computation through bit-shift and accumulation operations. As a result, ShiftLIF improves spike-level expressiveness without sacrificing the hardware-friendly nature of standard SNN computation. We evaluate ShiftLIF on 10 datasets spanning wireless, acoustic, motion, and visual sensing tasks. Results show that ShiftLIF consistently matches or exceeds the accuracy of existing multi-level spiking neurons while maintaining synaptic energy consumption close to standard binary LIF. These results indicate that ShiftLIF provides a favorable accuracy-efficiency trade-off for cross-modal edge sensing.

翻译：脉冲神经网络（SNN）因其事件驱动计算和时间滤波能力，在边缘感知领域具有广阔前景。然而，标准漏积分激发（LIF）神经元仅通过二进制脉冲进行通信，严重限制了表征能力。现有的多级脉冲神经元虽能提升信息传输效率，但通常采用与膜电位分布不匹配的均匀量化，或引入昂贵的突触乘法运算。本文提出ShiftLIF——一种将膜电位映射到对数间隔二次幂脉冲集的多级脉冲神经元。该设计在膜电位密集集中的小振幅区域提供更精细的表征，同时通过移位加累积运算实现无乘数的突触计算。因此，ShiftLIF在保持标准SNN计算硬件友好特性的同时，提升了脉冲级表达能力。我们在涵盖无线、声学、运动及视觉感知任务的10个数据集上评估了ShiftLIF。结果表明，ShiftLIF在维持接近标准二进制LIF突触能耗的前提下，其精度始终匹配或超越现有各类多级脉冲神经元。这些结果证明ShiftLIF为跨模态边缘感知提供了优越的精度-效率权衡。