Modern electronics products in domains such as virtual reality, augmented reality, home robots and cars deploy multiple cameras for advanced computer vision and AI use cases. These cameras are used for object detection, object tracking, object recognition, SLAM, hand tracking, eye tracking, gesture recognition, biometric authentication and a variety of other use cases, all enabled via traditional computer vision algorithms or neural network models. Camera frames produced for these algorithms do not need sophisticated image signal processing (such as that typically performed in ISP hardware) if the neural network models were trained with data collected without use of such image processing steps. Since these cameras used for AI use cases don’t need image signal processing, there is a scope to optimize the camera stack to obtain better performance, reducing the CPU load. In this talk, we will share optimization strategies to create a lightweight camera stack that can be executed on low-power devices to enable AI use cases.