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Hongwei Ren

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6 papers

2 author rows

AAAI Conference 2025 Conference Paper

Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks

Ziqing Wang
Yuetong Fang
Jiahang Cao
Hongwei Ren
Renjing Xu

Spiking Neural Networks (SNNs) are seen as an energy-efficient alternative to traditional Artificial Neural Networks (ANNs), but the performance gap remains a challenge. While this gap is narrowing through ANN-to-SNN conversion, substantial computational resources are still needed, and the energy efficiency of converted SNNs cannot be ensured. To address this, we present a unified training-free conversion framework that significantly enhances both the performance and efficiency of converted SNNs. Inspired by the biological nervous system, we propose a novel Adaptive-Firing Neuron Model (AdaFire), which dynamically adjusts firing patterns across different layers to substantially reduce the Unevenness Error - the primary source of error of converted SNNs within limited inference timesteps. We further introduce two efficiency-enhancing techniques: the Sensitivity Spike Compression (SSC) technique for reducing spike operations, and the Input-aware Adaptive Timesteps (IAT) technique for decreasing latency. These methods collectively enable our approach to achieve state-of-the-art performance with significant energy savings of up to 70.1%, 60.3%, and 43.1% on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Extensive experiments across 2D, 3D, event-driven classification tasks, object detection, and segmentation tasks, demonstrate the effectiveness of our method in various domains.

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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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EAAI Journal 2025 Journal Article

Finite-time consensus of leader-following multi-agent systems with event-triggered control strategy utilizing absolute velocity information

Guoliang Tan
Hongwei Ren
Feiqi Deng

This paper investigates the problem of finite-time consensus (FTC) of second-order multi-agent systems (MASs) with intrinsic nonlinear dynamics. A novel distributed event-triggered control (ETC) protocol has been proposed. This protocol achieves the control objectives in scenarios where both the velocity information of the leader and its neighboring agents is simultaneously absent, and introduces new triggering strategy, effectively reducing the demand for communication bandwidth and ensures the tracking of the leader’s trajectory. Subsequently, rigorous proofs are conducted using finite-time stability theory and Lyapunov stability theory to ensure leader-following consensus and establish the settling time while excluding Zeno behavior. Finally, through simulations involving multi-Unmanned Aerial Vehicle (multi-UAV) collaboration systems, the effectiveness of the proposed control protocol is demonstrated.

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NeurIPS Conference 2025 Conference Paper

Spiking Neural Networks Need High-Frequency Information

Yuetong Fang
Deming Zhou
Ziqing Wang
Hongwei Ren
zeng zecui
Lusong Li
Shibo Zhou
Renjing Xu

Spiking Neural Networks promise brain-inspired and energy-efficient computation by transmitting information through binary (0/1) spikes. Yet, their performance still lags behind that of artificial neural networks, often assumed to result from information loss caused by sparse and binary activations. In this work, we challenge this long-standing assumption and reveal a previously overlooked frequency bias: spiking neurons inherently suppress high-frequency components and preferentially propagate low-frequency information. This frequency-domain imbalance, we argue, is the root cause of degraded feature representation in SNNs. Empirically, on Spiking Transformers, adopting Avg-Pooling (low-pass) for token mixing lowers performance to 76. 73% on Cifar-100, whereas replacing it with Max-Pool (high-pass) pushes the top-1 accuracy to 79. 12%. Accordingly, we introduce Max-Former that restores high-frequency signals through two frequency-enhancing operators: (1) extra Max-Pool in patch embedding, and (2) Depth-Wise Convolution in place of self-attention. Notably, Max-Former attains 82. 39% top-1 accuracy on ImageNet using only 63. 99M parameters, surpassing Spikformer (74. 81%, 66. 34M) by +7. 58%. Extending our insight beyond transformers, our Max-ResNet-18 achieves state-of-the-art performance on convolution-based benchmarks: 97. 17% on CIFAR-10 and 83. 06% on CIFAR-100. We hope this simple yet effective solution inspires future research to explore the distinctive nature of spiking neural networks. Code is available: https: //github. com/bic-L/MaxFormer.

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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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