Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks

2026-03-13 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Advanced, extended

Summary

The "Stable Spike" method introduces a dual consistency optimization approach to enhance the recognition performance and generalization of Spiking Neural Networks (SNNs) by mitigating inherent inconsistencies in temporal spike dynamics. This technique efficiently decouples a stable spike skeleton from multi-timestep spike maps using hardware-friendly bitwise "AND" operations, capturing critical semantics while reducing noise. It then enforces unstable spike maps to converge to this stable skeleton, improving consistency across timesteps. Additionally, amplitude-aware spike noise is injected into the stable spike skeleton to diversify representations and promote generalization by encouraging perturbation-consistent predictions. The method is plug-and-play, requiring no modifications to SNN neuron models or architectures, and has demonstrated significant accuracy improvements, including up to 8.33% on neuromorphic object recognition tasks like DVS-Gesture under ultra-low latency (2 timesteps), while also slightly reducing power consumption.

Key takeaway

For AI Scientists and Research Scientists developing or deploying SNNs, consider integrating the Stable Spike method to significantly improve model accuracy and generalization, especially for low-latency neuromorphic applications. Your SNNs can achieve higher performance without architectural changes, potentially reducing power consumption and offering a plug-and-play solution to enhance existing models. Experiment with the balance coefficients (e.g., $\beta$, $\gamma$) and temperature ($\alpha$) to fine-tune performance for specific datasets and architectures.

Key insights

Dual consistency optimization via "AND" operations and amplitude-aware noise improves SNN performance and generalization.

Principles

• Temporal spike dynamics introduce inconsistency in SNNs.
• Consistency and diversity are crucial for SNN performance.
• Bitwise "AND" operations efficiently extract stable features.

Method

Decouple stable spike skeletons using bitwise AND on spike maps. Guide original spike maps to converge to this skeleton. Inject amplitude-aware spike noise into the stable skeleton to enhance diversity and generalization.

In practice

• Apply bitwise AND to SNN spike maps for feature stabilization.
• Integrate amplitude-aware noise for SNN generalization.
• Use MSE or KL divergence for consistency loss functions.

Topics

• Spiking Neural Networks
• Consistency Optimization
• Neuromorphic Computing
• Bitwise Operations
• Low-Latency Inference

Best for: AI Scientist, Research Scientist, AI Researcher, AI Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.