Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization

The gist The proposed Intrinsically Stable SNN (IS-SNN) architecture removes activation-normalization layers by enforcing intrinsic signal stability through topology-aware weight standardization and offline reparameterization.

Motivation and Problem Statement

Deep spiking neural networks (SNNs) often rely on batch normalization (BN), but advanced variants introduce runtime multiplications that weaken hardware efficiency. This reliance creates a tension with the hardware-efficiency motivation of neuromorphic computing. Simply removing activation normalization is not a viable solution because deep SNNs lacking statistics control face a signal-propagation barrier: catastrophic firing-rate decay. Without normalization, pre-activation variances can drift across layers, causing neuronal firing rates to either vanish or saturate. This disrupts signal propagation and can lead to severe performance degradation or training collapse.

IS-SNN Architecture and Stabilization Mechanism

The IS-SNN architecture is designed to stabilize the firing rate by controlling the pre-activation statistics layer by layer. It removes activation-normalization layers while maintaining competitive accuracy through intrinsic signal stabilization. This is achieved through two main components:

1. Weight Standardization (WS): This mechanism aims to keep the pre-activation scale close to a standard-normal reference, xpa ∼ N (0, 1). The original weights are dynamically standardized at the beginning of each training step using Eq. (7).

2. Modified Residual Connections: To control variance growth in residual networks, a modified residual connection is introduced where xl+1 = α · SN(f(xl)) + xl. The scaling factor γl is computed throughout the ResNet topology to manage this growth.

Performance and Experimental Results

Comprehensive experiments demonstrate that IS-SNN achieves performance competitive with or superior to advanced, computationally expensive dynamic BN variants across diverse architectures, including VGG, ResNet, and Transformer-based models. Notably, it scales to deep networks such as ResNet-152 and achieves 68.05% accuracy on ImageNet. Table 2 shows that IS-SNN maintains stable firing rates across layers both at initialization and after training, as shown in Fig. 1. Furthermore, the results indicate a favorable performance-efficiency trade-off for deep SNNs.

Hardware Efficiency and Resource Consumption

The IS-SNN architecture offers significant hardware benefits by decoupling normalization from the inference phase. This decoupling means the deployed IS-SNN is structurally identical to a raw BN-free network and requires no runtime normalization statistics tracking or normalization-related multiplications. A dedicated hardware analysis shows a 96.4% reduction in FPGA lookup table (LUT) resource consumption for neuron implementations. This saving is derived from replacing the dynamic scaling factor multiplication with an adder cost, yielding a theoretical saving of 93.75% for N=32.

Conclusion and Future Directions

IS-SNN provides a practical framework for building accurate and hardware-friendly deep neuromorphic systems. By removing computationally expensive normalization operations during inference, IS-SNN helps reduce the tension between high performance and hardware efficiency in deep SNNs. This work opens several promising avenues for future research. First, the stable signal-propagation behavior provided by IS-SNN can serve as a baseline for investigating trade-offs among firing rate, spike sparsity, and task performance in SNNs. Second, future research could adapt these intrinsic stabilization mechanisms to hardware-friendly online learning paradigms. Third, extending IS-SNN beyond vision benchmarks to sequence-centric tasks and to unsupervised or selfsupervised learning may further test the generality of BN-free SNNs. The connection between IS-SNN and homeostatic regulation also suggests that neuroscience inspired stability principles can provide useful guidance for efficient systems.

--- Page 1 ---

The gist The proposed Intrinsically Stable SNN (IS-SNN) architecture removes activation-normalization layers by enforcing intrinsic signal stability through topology-aware weight standardization and offline reparameterization.

How it works

The IS-SNN architecture is designed to stabilize the firing rate by controlling the pre-activation statistics layer by layer. It removes activation-normalization layers while maintaining competitive accuracy through intrinsic signal stabilization. This is achieved through two main components:

Key Technical Details

The calculation of the scaling factor γl depends on the network architecture. For plain nonresidual networks such as VGG, the output of layer l serves directly as the input to layer l + 1, and thus σ2 in,l+1 = σ2 out,l = σ2 g. The scaling factor for any layer l is therefore set to γl ≡ 1/σg. Conversely, residual networks compound variance through addition operations, and this growth is controlled by the modified residual connection.

Hardware Implementation Details

The hardware analysis shows that the IS-SNN neuron requires only an adder for membrane potential accumulation and a comparator for thresholding. This removes the runtime multiplication operators introduced by activation normalization. The theoretical reduction in resource consumption can be formalized as 0.5N squared - N / 0.5N squared = 1 - 1/N.

Experimental Validation

Comprehensive experiments show that IS-SNN achieves performance competitive with or superior to advanced, non-fusible normalization methods across diverse architectures. For ImageNet, the 128-epoch schedule yields 68.05% accuracy. The method's robustness to structural changes is confirmed by Table 4 and Fig. 4.

Limitations

IS-SNN removes inference-time normalization overhead through offline reparameterization, which assumes that the weights remain fixed after training. Thus, IS-SNN is particularly suitable for inference focused edge deployment. The hardware analysis focuses on the arithmetic and resource costs introduced by activation normalization, especially the runtime multiplications required by dynamic BN variants <ref:2606.

1. Weight Standardization for Signal Homeostasis: The IS-SNN enforces signal homeostasis through topology-aware weight standardization by deriving scaling factors from SNN signal propagation, neuron output variance, and residual variance growth under spiking dynamics. This allows the system to maintain stable firing rates across layers, preventing the catastrophic firing-rate decay that plagues normalization-free SNNs.

2. Offline Parameter Folding for Inference Efficiency: The architecture achieves inference efficiency by folding the standardization operations into static weights offline, which ensures that during deployment, the hardware only requires an adder for membrane potential accumulation and a comparator for thresholding, thus removing the runtime multiplication operators introduced by activation normalization.

3. Robust Performance on Deep Architectures: The IS-SNN achieves accuracy competitive with or superior to dynamic BN techniques across models like ResNet and Transformer-based networks, achieving a competitive accuracy of 68.05% on ImageNet and overcoming the severe depth limitations of prior BN-free attempts.

4. Enhanced Training Through Stability: The architecture maintains stable training dynamics across complex datasets, as evidenced by its ability to prevent convergence failures in VGG and ResNet architectures where naive BN-free baselines fail, supporting effective learning in deep structures.

5. Hardware Efficiency Gains: The system delivers a 96.4% reduction in FPGA lookup table resource consumption for neuron implementations by eliminating runtime normalization overhead, demonstrating a favorable performance-efficiency trade-off for neuromorphic systems on resource-constrained hardware.