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

Abstract

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.

Figures (8)

• Figure 1: Comparison of vanilla SNN spike maps and stable spike maps. Vanilla spike maps varied widely across timesteps, negatively affecting the overall representation; stable spike maps, decoupled by minimal & operation, consistently represented the feature skeleon. The event frames are from the CIFAR10-DVS dataset and the visualization shows the spike maps of the first layer in VGG-9 after averaging over all channels. Additional visualizations in Supplementary Material show that the stable spike consistently extracts the feature skeleton.
• Figure 2: The stable spikes decoupled by the minimal & operation are used as the anchor. On the one hand, we promote the variable original spike maps to converge to the stable spike firing rate skeleton, i.e., spike map consistency. On the other hand, we introduce amplitude-aware spike noise to the stable spiking firing rate to preserve the key semantics and increase the feature diversity, allowing the SNN to be insensitive to the perturbation and promote generalization, i.e., perturbation consistency.
• Figure 3: Comparison of spike firing rates. Our spike firing rate shows a clearer feature profile compared to the vanilla SNN by converging to the stable spike and reducing spike interference. Additionally, the proposed amplitude-aware spike noise preserves the key semantic features of stable spikes while increasing feature diversity, thereby improving generalization. To reflect the implementation, we visualize the SNN backbone output of VGG-9 with a spike map size of $6 \times 6$.
• Figure 4: Performance (%) of different balance coefficients $\beta$ and $\gamma$ on DVS-Gesture.
• Figure 5: Visualization of loss landscapes. (a) (c) The loss landscape of the vanilla SNN exhibits multiple local minima and saddle points. This complexity makes optimization susceptible to falling into local optima. (b) (d) Our method has a smoother and more centralized loss landscape with a clear global minimum, which makes optimization stable and convergent.