FPGA是边缘计算技术的主要平台之一，在边缘侧图像识别领域具有广阔应用前景。为实现实时图像识别的端侧部署，本文设计并实现了一种基于国产FPGA与自主设计类ASIC架构的嵌入式系统。软件层面，提出了一种轻量级神经网络NexusEdgeNet，以仅0.184MB参数量，对39类农田病害图像的识别准确率达到94.22%。硬件层面，创新性地设计了一款完全采用Verilog HDL描述的类ASIC加速器，采用分布式存储，不依赖外存储器，支持任意形状卷积、池化及全连接等算子。通过近存并行计算、流水线、滑动卷积窗口及双缓冲存储等优化策略，该神经网络加速器在中科亿海微EP6HL130 FPGA上实现了399 FPS的高推理帧率，大幅降低了逻辑资源使用量，计算资源利用率高达85%。系统集成图像采集、处理与显示链路，支持视频流的实时处理与识别。本系统在保持高精度的同时，具备优异的实时性与资源效率，为国产FPGA在边缘计算中的低成本应用提供了有价值的实践方案。

Field-Programmable Gate Arrays (FPGAs) are key platforms for edge computing and have broad potential in edge-side image recognition. This paper presents an embedded system based on a domestically produced FPGA and a self-designed ASIC-like architecture for real-time edge deployment. On the software side, a lightweight neural network named NexusEdgeNet is proposed. It achieves 94.22% accuracy on 39 farmland disease categories with only 0.184 MB of parameters. On the hardware side, an ASIC-like accelerator fully described in Verilog is designed. It adopts a distributed on-chip memory structure, eliminating external memory access, and supports arbitrary-shaped convolution, pooling, and fully connected operations. Several optimization techniques are applied, including near-memory parallel computing, pipelining, sliding convolution windows, and double buffering. The accelerator reaches 399 FPS inference speed on the EP6HL130 FPGA, with 85% resource utilization and significantly reduced logic consumption. The system integrates image acquisition, processing, and display, supporting real-time video stream recognition. It maintains high accuracy while achieving excellent real-time performance and resource efficiency. This work provides a practical, low-cost solution for edge computing applications based on domestic FPGAs.