ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization

2026-05-03

AI Summary

This work addresses the limited representational capacity of traditional leaky integrate-and-fire (LIF) neurons, which support only binary spikes, and the high computational overhead or reliance on uniform quantization in existing multi-level spiking schemes. The authors propose ShiftLIF, a novel approach that employs logarithmically spaced, power-of-two quantization levels for membrane potentials, enabling fine-grained, low-magnitude multi-level spike encoding. Crucially, ShiftLIF facilitates multiplier-free synaptic computation through bit-shift and accumulation operations. This design achieves a remarkable balance between accuracy and energy efficiency: it matches or exceeds state-of-the-art performance across ten diverse datasets spanning wireless, acoustic, motion, and visual sensing modalities, while maintaining synaptic energy consumption nearly equivalent to that of standard binary LIF neurons.

Abstract

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.

Problem

• spiking neural networks
• multi-level spiking neurons
• membrane potential quantization
• hardware efficiency
• representational capacity

Innovation

• ShiftLIF
• multi-level spiking neurons
• power-of-two quantization
• bit-shift operations
• edge sensing