发表机构
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出LiteEvent-AE轻量级事件驱动自编码器，可在低功耗边缘硬件上高效压缩事件流数据，精度优于YOLOv9，能耗仅为其约1/726.3，实现低延迟节能的高速感知。
AI 中文摘要
事件驱动视觉已成为一种极具潜力的节能型人工智能（AI）范式，它能提供稀疏、低延迟的视觉信号，减少冗余数据处理，支持可持续的边缘计算。然而，事件流的异步性和易噪声特性给传统深度学习模型带来了挑战，传统深度学习模型的计算量通常过大，无法在低功耗嵌入式平台上运行。本研究提出了一种紧凑且可配置的事件驱动自编码器，该编码器可高效压缩神经形态数据，同时保留下游推理所需的关键时空结构。该架构集成了轻量级卷积编码，在自适应事件阈值处理下具备稳健性能，并配备了极简分类器头，可在不降低识别精度的前提下大幅降低计算成本。在智能事件人脸数据集（Smart Event Face Dataset, SEFD）和事件驱动交叉数据集（Event-Based Crossing Dataset, EBCD）上的大量评估表明，所提出的框架与YOLOv9相比具有相当或更优的精度，同时所需参数最多减少35.6倍。为评估实际应用中的可持续性，该模型被部署在资源受限的硬件上：树莓派4B（Raspberry Pi 4B）和英伟达Jetson Nano（NVIDIA Jetson Nano）。在英伟达Jetson Nano上，它实现了44.8 FPS的实时吞吐量；在树莓派4B CPU上，50%自编码器分类器在评估推理工作负载下的能耗为16.19 J，在相同评估协议下，其能耗比YOLOv9低约726.3倍。这些结果表明，紧凑的事件驱动模型有望推动环保型低功耗AI系统的发展，为自主、移动和嵌入式计算环境中的高速感知提供支持。
英文摘要
Event-based vision has emerged as a promising paradigm for energy-aware artificial intelligence (AI), offering sparse, low-latency visual signals that reduce redundant data processing and support sustainable edge computing. However, the asynchronous and noise-prone nature of event streams creates challenges for conventional deep learning models, which are often too computationally intensive for low-power embedded platforms. This work presents a compact and configurable event-driven autoencoder that efficiently compresses neuromorphic data while preserving essential spatiotemporal structure for downstream inference. The architecture integrates lightweight convolutional encoding with robust performance under adaptive event thresholding and a minimal classifier head, enabling substantial reductions in computational cost without degrading recognition fidelity. Extensive evaluations on the Smart Event Face Dataset (SEFD) and Event-Based Crossing Dataset (EBCD) show that the proposed framework achieves competitive or superior accuracy compared to YOLOv9 while requiring up to 35.6$\times$ fewer parameters. To assess real-world sustainability, the model is deployed on resource-constrained hardware: a Raspberry Pi 4B and a NVIDIA Jetson Nano. On NVIDIA Jetson Nano, it delivers real-time throughput of 44.8 FPS. On a Raspberry Pi 4B CPU, the 50\% autoencoder classifier consumes 16.19 J for the evaluated inference workload, corresponding to approximately 726.3$\times$ lower energy consumption than YOLOv9 under the same evaluation protocol. These results demonstrate the potential of compact event-driven models to advance environmentally conscious, low-power AI systems for high-speed perception in autonomous, mobile, and embedded computing environments.
