Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its biological counterpart. These sensors process information significantly different than the traditional RGB sensors. Specifically, the sensory information generated by event-based image sensors are orders of magnitude sparser compared to that of RGB sensors. The first generation of neuromorphic image sensors, Dynamic Vision Sensor (DVS), are inspired by the computations confined to the photoreceptors and the first retinal synapse. In this work, we highlight the capability of the second generation of neuromorphic image sensors, Integrated Retinal Functionality in CMOS Image Sensors (IRIS), which aims to mimic full retinal computations from photoreceptors to output of the retina (retinal ganglion cells) for targeted feature-extraction. The feature of choice in this work is Object Motion Sensitivity (OMS) that is processed locally in the IRIS sensor. Our results show that OMS can accomplish standard computer vision tasks with similar efficiency to conventional RGB and DVS solutions but offers drastic bandwidth reduction. This cuts the wireless and computing power budgets and opens up vast opportunities in high-speed, robust, energy-efficient, and low-bandwidth real-time decision making.

翻译：神经形态（事件驱动）图像传感器从人眼视网膜获得灵感，构建了一种能以高度模拟生物对应方式处理视觉刺激的电子器件。这类传感器处理信息的方式与传统RGB传感器存在显著差异。具体而言，事件驱动图像传感器生成的感知信息稀疏程度较RGB传感器高出数个数量级。第一代神经形态图像传感器——动态视觉传感器（DVS）——受限于光感受器及视网膜第一级突触的计算机制。本研究着重凸显第二代神经形态图像传感器——CMOS图像传感器集成视网膜功能（IRIS）——的能力，该传感器旨在模拟从光感受器到视网膜输出（视网膜神经节细胞）的完整视网膜计算过程，以实现目标特征提取。本文选定的特征为物体运动敏感性（OMS），该特征由IRIS传感器进行局部处理。研究结果表明，OMS在完成标准计算机视觉任务时，能达到与传统RGB及DVS方案相当的效率，但能实现带宽的极大幅度缩减。这一特性降低了无线传输和计算功耗预算，为高速、鲁棒、节能且低带宽的实时决策开辟了广阔前景。