δLTA: Decoupling Camera Sampling from Processing to Avoid Redundant Computations in the Vision Pipeline

Continuous Vision (CV) systems are essential for emerging applications like Autonomous Driving (AD) and Augmented/Virtual Reality (AR/VR). A standard CV System-on-a-Chip (SoC) pipeline includes a frontend for image capture and a backend for executing vision algorithms. The frontend typically captures successive similar images with gradual positional and orientational variations. As a result, many regions between consecutive frames yield nearly identical results when processed in the backend. Despite this, current systems process every image region at the camera’s sampling rate, overlooking the fact that the actual rate of change in these regions could be significantly lower. In this work, we introduce δ LTA (δont’t Look Twice, it’s Alright), a novel frontend that decouples camera frame sampling from backend processing by extending the camera with the ability to discard redundant image regions before they enter subsequent CV pipeline stages. δ LTA informs the backend about the image regions that have notably changed, allowing it to focus solely on processing these distinctive areas and reusing previous results to approximate the outcome for similar ones. As a result, the backend processes each image region using different processing rates based on its temporal variation. δ LTA features a new Image Signal Processing (ISP) design providing similarity filtering functionality, seamlessly integrated with other ISP stages to incur zero-latency overhead in the worst-case scenario. It also offers an interface for frontend-backend collaboration to fine-tune similarity filtering based on the application requirements. To illustrate the benefits of this novel approach, we apply it to a state-of-the-art CV localization application, typically employed in AD and AR/VR. We show that δ LTA removes a significant fraction of unneeded frontend and backend memory accesses and redundant backend computations, which reduces the application latency by 15.22% and its energy consumption by 17%.
This work has been supported by the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency (MCIN/AEI) under grant PID2020-113172RB-I00, the ICREA Academia program, and the FPU grant FPU18/04413.