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Bojun Cheng

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4 papers

2 author rows

TMLR Journal 2026 Journal Article

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba

Yulong Huang
Jianxiong Tang
Chao Wang
Ziyi Wang
Jianguo Zhang
Zhichao Lu
Bojun Cheng
Luziwei Leng

Large Language Models (LLMs) have achieved remarkable performance across tasks but remain energy-intensive due to dense matrix operations. Spiking neural networks (SNNs) improve energy efficiency by replacing dense matrix multiplications with sparse accumulations. Their sparse spike activity enables efficient LLMs deployment on edge devices. However, prior SNN-based LLMs often sacrifice performance for efficiency, and recovering accuracy typically requires full pretraining, which is costly and impractical. To address this, we propose SpikingMamba, an energy-efficient SNN-based LLMs distilled from Mamba that improves energy efficiency with minimal accuracy sacrifice. SpikingMamba integrates two key components: (a) SI-LIF, a signed-integer spiking neuron that preserves semantic polarity through signed multi-level spike representations. (b) A training-exclusive Smoothed Gradient Compensation (SGC) path mitigating quantization loss while preserving spike-driven efficiency. We employ a single-stage distillation strategy to transfer the zero-shot ability of pretrained Mamba and further enhance it via reinforcement learning (RL). Experiments show that SpikingMamba-1.3B achieves a 4.76$\times$ energy benefit, with only a 4.78\% zero-shot accuracy gap compared to the original Mamba. The model achieves a further 2.55\% accuracy improvement after RL, narrowing the performance gap from 4.78\% to 2.23\%.

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ICRA Conference 2025 Conference Paper

E2B: A Single Modality Point-Based Tracker with Event Cameras

Hongwei Ren
Zhuo Li
Aiersi Tuerhong
Haobo Liu
Fei Liang
Yongxiang Feng
Wenhui Wang 0001
Yaoyuan Wang

High-speed object tracking holds significant relevance across robotic domains, such as drones and autonomous driving. Compared to conventional cameras, event cameras are equipped with the ability to capture object motion information at exceptionally high temporal resolution with relatively low power consumption and remain immune from motion-blurring effects. Regrettably, many existing methods adopt a framebased approach by stacking events into Event Frame, which overlooks the sparsity and high temporal resolution of events. This approach is also reliant on the huge pre-training backbone and reaches a performance plateau but demands unrealistically large networks and high power consumption, rendering it impractical for real-time applications in battery-constrained robotic scenarios. In this paper, we propose an efficient and effective single-modality tracker using Point Cloud representation named E2B (Event to Box). By directly handling the raw output of event cameras without dataformat transformation, E2B leverages events' coordinate guidance to accurately map Event Cloud features to 2D bounding boxes. Moreover, E2B incorporates the pyramid structure into the multi-stage feature extraction architecture to effectively track objects across diverse scales. In the experiments, E2B performs outstandingly on two large-scale and one synthetic event-based tracking datasets, covering both indoor and outdoor environments, as well as rigid and non-rigid objects.

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ICML Conference 2024 Conference Paper

CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Yulong Huang
Xiaopeng Lin
Hongwei Ren
Haotian Fu
Yue Zhou
Zunchang Liu
Biao Pan
Bojun Cheng

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior efficiency and capability to process temporal information. However, it remains a challenge to train SNNs due to their undifferentiable spiking mechanism. The surrogate gradients method is commonly used to train SNNs, but often comes with an accuracy disadvantage over ANNs counterpart. We link the degraded accuracy to the vanishing of gradient on the temporal dimension through the analytical and experimental study of the training process of Leaky Integrate-and-Fire (LIF) Neuron-based SNNs. Moreover, we propose the Complementary Leaky Integrate-and-Fire (CLIF) Neuron. CLIF creates extra paths to facilitate the backpropagation in computing temporal gradient while keeping binary output. CLIF is hyperparameter-free and features broad applicability. Extensive experiments on a variety of datasets demonstrate CLIF’s clear performance advantage over other neuron models. Furthermore, the CLIF’s performance even slightly surpasses superior ANNs with identical network structure and training conditions. The code is available at https: //github. com/HuuYuLong/Complementary-LIF.

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ICLR Conference 2024 Conference Paper

SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition

Hongwei Ren
Yue Zhou
Xiaopeng Lin
Yulong Huang
Haotian Fu
Jie Song
Bojun Cheng

Event cameras are bio-inspired sensors that respond to local changes in light intensity and feature low latency, high energy efficiency, and high dynamic range. Meanwhile, Spiking Neural Networks (SNNs) have gained significant attention due to their remarkable efficiency and fault tolerance. By synergistically harnessing the energy efficiency inherent in event cameras and the spike-based processing capabilities of SNNs, their integration could enable ultra-low-power application scenarios, such as action recognition tasks. However, existing approaches often entail converting asynchronous events into conventional frames, leading to additional data mapping efforts and a loss of sparsity, contradicting the design concept of SNNs and event cameras. To address this challenge, we propose SpikePoint, a novel end-to-end point-based SNN architecture. SpikePoint excels at processing sparse event cloud data, effectively extracting both global and local features through a singular-stage structure. Leveraging the surrogate training method, SpikePoint achieves high accuracy with few parameters and maintains low power consumption, specifically employing the identity mapping feature extractor on diverse datasets. SpikePoint achieves state-of-the-art (SOTA) performance on four event-based action recognition datasets using only 16 timesteps, surpassing other SNN methods. Moreover, it also achieves SOTA performance across all methods on three datasets, utilizing approximately 0.3 % of the parameters and 0.5 % of power consumption employed by artificial neural networks (ANNs). These results emphasize the significance of Point Cloud and pave the way for many ultra-low-power event-based data processing applications.

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