With　the　rapid　development　of　deep　learning,　the　neural　network　of　semantic　segmentation　has　been　developed　towards　the　miniaturization　of　the　network　structure　and　the　lightweight　development　of　the　network　model.　At　the　same　time,　FPGA-based　neural　network　hardware　accelerators　have　been　proposed.　The　situation　that　the　network　module　is　too　complex　and　computationally　intensive　to　implement　and　apply　on　edge　platforms　is　gradually　being　solved.　However,　the　implementation　of　real-time　processing　network　on　the　edge　platform　is　still　of　great　significance　in　many　areas,　such　as　robots,　UAVs,　driverless,　etc.　In　this　paper,　a　lightweight　semantically　segmented　neural　network　Efficient　neural　network　(E-Net)　is　designed　and　implemented　on　the　image　acquisition　board　with　Zynq　7035　FPGA　as　processing　unit,　which　achieves　the　frame　rate　of　32.9　FPS　and　meets　the　requirements　of　real-time　processing.　©　2021　ACM.