This article explores the potential of non-von Neumann flexible neuromorphic vision sensors to address the limitations of traditional machine vision systems. It begins by comparing the human visual system with traditional machine vision systems, highlighting the advantages of the human system in terms of efficiency, robustness, and low power consumption. The article then delves into the structure and functionality of the retina, emphasizing its role in preprocessing visual information and its curved structure, which offers advantages in optical performance and data processing. The article discusses the challenges faced by traditional machine vision systems, particularly in handling high-precision and high-speed tasks in complex real-world scenarios. It introduces the concept of non-von Neumann computing architectures, which integrate computational capabilities into sensors, reducing the need for data transmission and improving overall system efficiency. The article reviews various non-von Neumann architectures, including analog and digital circuits, and optical neural networks, detailing their principles and applications. The section on materials and manufacturing methods focuses on the fabrication of curved sensor arrays, highlighting the use of new materials like graphene, transition metal dichalcogenides (TMDCs), and ferroelectric materials to enhance the performance and flexibility of photodetectors. The article concludes by analyzing the current state of research and future perspectives on non-von Neumann flexible neuromorphic vision sensors, emphasizing their potential to revolutionize machine vision systems.This article explores the potential of non-von Neumann flexible neuromorphic vision sensors to address the limitations of traditional machine vision systems. It begins by comparing the human visual system with traditional machine vision systems, highlighting the advantages of the human system in terms of efficiency, robustness, and low power consumption. The article then delves into the structure and functionality of the retina, emphasizing its role in preprocessing visual information and its curved structure, which offers advantages in optical performance and data processing. The article discusses the challenges faced by traditional machine vision systems, particularly in handling high-precision and high-speed tasks in complex real-world scenarios. It introduces the concept of non-von Neumann computing architectures, which integrate computational capabilities into sensors, reducing the need for data transmission and improving overall system efficiency. The article reviews various non-von Neumann architectures, including analog and digital circuits, and optical neural networks, detailing their principles and applications. The section on materials and manufacturing methods focuses on the fabrication of curved sensor arrays, highlighting the use of new materials like graphene, transition metal dichalcogenides (TMDCs), and ferroelectric materials to enhance the performance and flexibility of photodetectors. The article concludes by analyzing the current state of research and future perspectives on non-von Neumann flexible neuromorphic vision sensors, emphasizing their potential to revolutionize machine vision systems.