Dynamic Vision Sensors (DVS) exhibit exceptional dynamic range and low power consumption, making them ideal for edge applications in the Internet of Video Things (IoVT). However, their output is often degraded by spurious Background Activity (BA) noise, leading to unnecessary computational overhead. This paper proposes SNNF, a near-sensor BA noise filter that integrates a compact Event-Based Binary Image (EBBI) representation, a parallel memory architecture, and a single-layer Spiking Neural Network (SNN) classifier. Trained on representative DVS data, the SNN distinguishes signal events from noise with an AUC of 0.89 on standard datasets. The binary-array-based EBBI eliminates timestamp dependency, significantly reducing memory footprint. Moreover, the SNN's spike-based computation replaces power-hungry multipliers with simple accumulation logic and minimizes inter-neuron data width, resulting in an extremely hardware-efficient design. FPGA implementation results show that SNNF reduces memory and logic resources to approximately 11% and 40%, respectively of state-of-the-art filters, while achieving a throughput of 29 Mega events per second (Meps). In a 65 nm CMOS ASIC implementation, SNNF achieves 44.4 Meps with an area and power consumption of only ~13% and <5% of the corresponding ANN-based designs. These results demonstrate that SNNF provides an excellent balance between filtering accuracy and hardware efficiency, making it highly suitable for resource-constrained, near-sensor deployment.

翻译：摘要：动态视觉传感器（DVS）具有出色的动态范围和低功耗特性，使其成为视频物联网（IoVT）边缘应用的理想选择。然而，其输出常常受到虚假的背景活动（BA）噪声的干扰，导致不必要的计算开销。本文提出SNNF，一种近传感器BA噪声滤波器，它集成了紧凑的基于事件的二值图像（EBBI）表示、并行存储架构和单层脉冲神经网络（SNN）分类器。在代表性DVS数据上训练后，该SNN在标准数据集上以AUC 0.89区分信号事件与噪声。基于二值阵列的EBBI消除了时间戳依赖，显著降低了内存占用。此外，SNN的脉冲计算用简单的累加逻辑取代了高功耗的乘法器，并最小化神经元间数据位宽，从而实现了极其硬件高效的设计。FPGA实现结果表明，SNNF将存储和逻辑资源分别减少至现有最优滤波器的约11%和40%，同时实现了每秒2900万事件（Meps）的吞吐量。在65纳米CMOS ASIC实现中，SNNF达到44.4 Meps，其面积和功耗仅为相应基于ANN的设计的约13%和小于5%。这些结果表明SNNF在滤波精度与硬件效率之间实现了出色平衡，使其高度适用于资源受限的近传感器部署场景。