arXiv subjects

Dewei Bai

Publications and source records attributed to Dewei Bai.

4 recordsLinked to original sources

arXiv2026

Learnable Burst Quantization for Expressive and Efficient Spiking Neural Networks

Binary spikes provide only two neuronal output states per timestep, limiting the response capacity of spiking neural networks (SNNs) under short simulation horizons. Burst neurons expand this response space, but their threshold spacing is typically fixed before training, leaving layer-specific burst resolution outside end-to-end optimization. We propose Learnable Burst Quantization (LBQ), which formulates burst emission as saturated uniform quantization with a positive, layer-wise learnable step. ReLSG-ET, a rectified-linear surrogate gradient with exponential tails, provides gradient support throughout and beyond the active burst range, thereby enabling joint optimization of synaptic weights and burst resolution. At inference, LBQ absorbs each learned step into downstream weights and decomposes integer burst levels into binary bit planes, accumulating only the non-zero planes. This changes the synaptic accumulation count for a level $S$ from $S$ to $\operatorname{popcount}(S)$. At two timesteps, LBQ achieves 97.45\% on CIFAR-10 and 82.82\% on CIFAR-100 with ResNet-20, and 73.67\% on ImageNet-1K with ResNet-34. On CIFAR-10, it comes within 0.07 percentage points of the 97.52\% ResNet-20 ANN reference; at $N_{\max}=5$, bit-plane execution reduces unary-equivalent synaptic accumulations by 40.52\% relative to unary execution. Controlled ablations isolate the benefits of learned quantization and ReLSG-ET, while layer-wise analyses reveal selective burst allocation across network depth. Results on CIFAR10-DVS and DVS128-Gesture extend the evidence to event-driven recognition. LBQ therefore couples adaptive burst resolution and accurate inference with an algebraically equivalent bit-sparse synaptic execution path.

cs.CV↗

arXiv2026

Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks

Spiking Neural Networks (SNNs) are widely regarded as an energy-efficient paradigm for modeling and processing temporal and event-driven information. Incorporating delays in SNNs has been proven to be an effective mechanism for improving spike alignment in event-driven tasks. However, existing delay learning approaches predominantly assign static delays to individual synapses, resulting in a large number of delay parameters and limited adaptability to input-dependent activity dynamics. To this end, we propose a Congestion-Aware Dynamic Axonal Delay (CADAD) mechanism, which decomposes the delay into a channel-wise static base delay for temporal structuring and a global, activity-conditioned shift that dynamically regulates the state update rate under varying spike intensities. The delay parameters are learned using differentiable linear interpolation and discretized at inference time, preserving the benefits of dynamic delay modulation while incurring only minimal additional cost. Experiments on speech benchmarks, including the Spiking Heidelberg Dataset, Spiking Speech Commands, and Google Speech Commands, demonstrate that introducing congestion-aware delays into synaptic signal transmission effectively improves accuracy on temporal tasks, notably achieving 93.75% accuracy on SHD, 80.69% accuracy on SSC, and 95.58% on GSC-35, while reducing the parameter count by approximately 50% compared to state-of-the-art delay-based methods with the same architecture.

cs.LG↗

arXiv2026

BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning

Spiking Vision Transformers (S-ViTs) offer a promising framework for energy-efficient visual learning. However, existing designs remain limited by two fundamental issues: the restricted information capacity of binary spike coding and the dense token interactions introduced by global self-attention. To address these challenges, this work proposes BSViT, a burst spiking-driven Vision Transformer featuring a Dual-Channel Burst Spiking Self-Attention (DBSSA) mechanism. DBSSA encodes queries with binary spikes and keys with burst spikes to enhance representational capacity. The value pathway adopts dual excitatory and inhibitory binary channels, enabling signed modulation and richer spike interactions. Importantly, the entire attention operation preserves addition-only computation, ensuring compatibility with energy-efficient neuromorphic hardware. To further reduce spike activity and incorporate spatial priors, a patch adjacency masking strategy is introduced to restrict attention to local neighborhoods, resulting in structure-aware sparsity and reduced computational overhead. In addition, burst spike coding is systematically integrated across the network to increase spike-level representational capacity beyond conventional binary spiking. Extensive experiments on both static and event-based vision benchmarks demonstrate that BSViT consistently outperforms existing spiking Transformers in accuracy while maintaining competitive energy efficiency.

cs.CV↗

arXiv2026

Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models

Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token interactions remain fundamentally misaligned with the sparse and event-driven nature of spiking neural computation. To address this limitation, we propose Vision SmolMamba, an energy-efficient spiking state-space architecture that integrates spike-driven dynamics with linear-time selective recurrence. The key idea is a Spike-Guided Spatio-Temporal Token Pruner (SST-TP), which estimates token importance using both spike activation strength and first-spike latency. This mechanism progressively removes redundant tokens while preserving salient spatio-temporal information, enabling efficient scaling with token sparsity. Based on this mechanism, the proposed SmolMamba block incorporates spike events directly into bidirectional state-space recurrence, forming a spiking state-space vision backbone for efficient long-range modeling. Extensive experiments on both static and event-based benchmarks, including ImageNet-1K, CIFAR10/100, CIFAR10-DVS, and DVS128 Gesture, demonstrate that Vision SmolMamba consistently achieves superior accuracy-efficiency trade-offs. In particular, it reduces the estimated energy cost by at least 1.5x compared with prior spiking Transformer baselines and a Spiking Mamba variant while maintaining competitive or improved accuracy. These results demonstrate that combining spike-guided token sparsity with state-space modeling offers a scalable and energy-efficient paradigm for spiking vision systems.

cs.CV↗