  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
