Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal pattern capture capabilities, they also incur inherent inconsistencies that severely compromise representation. In this paper, we perform dual consistency optimization via Stable Spike to mitigate this problem, thereby improving the recognition performance of SNNs. With the hardware-friendly ``AND" bit operation, we efficiently decouple the stable spike skeleton from the multi-timestep spike maps, thereby capturing critical semantics while reducing inconsistencies from variable noise spikes. Enforcing the unstable spike maps to converge to the stable spike skeleton significantly improves the inherent consistency across timesteps. Furthermore, we inject amplitude-aware spike noise into the stable spike skeleton to diversify the representations while preserving consistent semantics. The SNN is encouraged to produce perturbation-consistent predictions, thereby contributing to generalization. Extensive experiments across multiple architectures and datasets validate the effectiveness and versatility of our method. In particular, our method significantly advances neuromorphic object recognition under ultra-low latency, improving accuracy by up to 8.33\%. This will help unlock the full power consumption and speed potential of SNNs.

翻译：尽管脉冲神经网络（SNNs）的时序脉冲动力学使其具备低功耗时序模式捕获能力，但也带来了固有的不一致性，严重损害了其表征能力。本文通过稳定脉冲进行双重一致性优化以缓解该问题，从而提升SNNs的识别性能。利用硬件友好的“与”位运算，我们高效地从多时间步脉冲图中解耦出稳定脉冲骨架，从而在捕获关键语义的同时减少来自可变噪声脉冲的不一致性。强制不稳定脉冲图向稳定脉冲骨架收敛，显著提升了跨时间步的固有一致性。此外，我们在稳定脉冲骨架中注入幅度感知的脉冲噪声以丰富表征，同时保持一致的语义。该方法促使SNN产生扰动一致的预测，从而有助于提升泛化能力。跨多种架构与数据集的广泛实验验证了本方法的有效性和通用性。特别地，本方法在超低延迟下显著推进了神经形态物体识别任务，准确率最高提升8.33%。这将有助于充分释放SNNs在功耗与速度方面的潜力。