CMOS Image Sensors (CIS) are fundamental to emerging visual computing applications. While conventional CIS are purely imaging devices for capturing images, increasingly CIS integrate processing capabilities such as Deep Neural Network (DNN). Computational CIS expand the architecture design space, but to date no comprehensive energy model exists. This paper proposes CamJ, a detailed energy modeling framework that provides a component-level energy breakdown for computational CIS and is validated against nine recent CIS chips. We use CamJ to demonstrate three use-cases that explore architectural trade-offs including computing in vs. off CIS, 2D vs. 3D-stacked CIS design, and analog vs. digital processing inside CIS. The code of CamJ is available at: https://github.com/horizon-research/CamJ

翻译：摘要：CMOS图像传感器是新兴视觉计算应用的基础。传统CMOS图像传感器是纯粹的成像器件，用于捕获图像，但如今越来越多的CMOS图像传感器集成了深度神经网络等处理能力。计算型CMOS图像传感器拓展了架构设计空间，然而迄今尚无全面的能量模型。本文提出CamJ，一个详细的能量建模框架，可为计算型CMOS图像传感器提供组件级能量分解，并基于九个近期CMOS图像传感器芯片进行了验证。我们利用CamJ展示了三个用例，探索了架构权衡，包括CMOS图像传感器内计算与传感器外计算、2D堆叠与3D堆叠CMOS图像传感器设计，以及CMOS图像传感器内部的模拟处理与数字处理。CamJ的代码开源地址为：https://github.com/horizon-research/CamJ