This article discusses the development of non-von Neumann flexible neuromorphic vision sensors, which aim to mimic the human visual system to improve machine vision performance. The human visual system is highly efficient in processing visual information with low redundancy and power consumption, while traditional machine vision systems based on the von Neumann architecture face challenges in data processing speed, algorithmic efficiency, and power consumption. Non-von Neumann architectures, which integrate computation and sensing, offer a promising solution by reducing data transfer and enabling more efficient processing. The human visual system consists of the retina and visual cortex. The retina processes light signals into electrical signals, which are then transmitted to the visual cortex for further processing. The retina's curved structure allows for efficient light focusing and preprocessing, while its hierarchical structure enables the processing of visual information in different layers. The retina also has specialized cells, such as cone cells and rod cells, which are responsible for color discrimination and low-light vision, respectively. Neuromorphic vision sensors, inspired by the human visual system, use event-based and data-driven principles to capture and process visual information. These sensors reduce redundancy and improve efficiency by focusing on changes in the visual scene rather than processing entire frames. Examples include the Dynamic Vision Sensor (DVS) and the Asynchronous Time-based Image Sensor (ATIS), which have been developed for various applications in machine vision and robotics. Non-von Neumann computing architectures, such as resistive crossbar arrays and memristive neural networks, offer advantages in terms of computational efficiency, power consumption, and integration. These architectures integrate computation and sensing, enabling more efficient processing of visual information. Memristive neural networks, in particular, can store weights and perform computations in parallel, making them suitable for complex neural networks. The article also discusses the challenges in developing non-von Neumann flexible neuromorphic vision sensors, including the need for flexible electronics, efficient data processing, and integration with existing machine vision systems. The use of new materials, such as graphene and two-dimensional materials, is explored for their potential in improving photodetector performance and enabling flexible sensor arrays. In conclusion, non-von Neumann flexible neuromorphic vision sensors offer a promising solution to the limitations of traditional machine vision systems by mimicking the human visual system's efficiency and adaptability. These sensors have the potential to significantly improve the performance of machine vision systems in various applications, including autonomous driving, robotics, and industrial inspection.This article discusses the development of non-von Neumann flexible neuromorphic vision sensors, which aim to mimic the human visual system to improve machine vision performance. The human visual system is highly efficient in processing visual information with low redundancy and power consumption, while traditional machine vision systems based on the von Neumann architecture face challenges in data processing speed, algorithmic efficiency, and power consumption. Non-von Neumann architectures, which integrate computation and sensing, offer a promising solution by reducing data transfer and enabling more efficient processing. The human visual system consists of the retina and visual cortex. The retina processes light signals into electrical signals, which are then transmitted to the visual cortex for further processing. The retina's curved structure allows for efficient light focusing and preprocessing, while its hierarchical structure enables the processing of visual information in different layers. The retina also has specialized cells, such as cone cells and rod cells, which are responsible for color discrimination and low-light vision, respectively. Neuromorphic vision sensors, inspired by the human visual system, use event-based and data-driven principles to capture and process visual information. These sensors reduce redundancy and improve efficiency by focusing on changes in the visual scene rather than processing entire frames. Examples include the Dynamic Vision Sensor (DVS) and the Asynchronous Time-based Image Sensor (ATIS), which have been developed for various applications in machine vision and robotics. Non-von Neumann computing architectures, such as resistive crossbar arrays and memristive neural networks, offer advantages in terms of computational efficiency, power consumption, and integration. These architectures integrate computation and sensing, enabling more efficient processing of visual information. Memristive neural networks, in particular, can store weights and perform computations in parallel, making them suitable for complex neural networks. The article also discusses the challenges in developing non-von Neumann flexible neuromorphic vision sensors, including the need for flexible electronics, efficient data processing, and integration with existing machine vision systems. The use of new materials, such as graphene and two-dimensional materials, is explored for their potential in improving photodetector performance and enabling flexible sensor arrays. In conclusion, non-von Neumann flexible neuromorphic vision sensors offer a promising solution to the limitations of traditional machine vision systems by mimicking the human visual system's efficiency and adaptability. These sensors have the potential to significantly improve the performance of machine vision systems in various applications, including autonomous driving, robotics, and industrial inspection.