CSRAP: Enhanced Canvas Attention Scheduling for Real-Time Mission Critical Perception
Md Iftekharul Islam Sakib, Yigong Hu, Tarek Abdelzaher

TL;DR
This paper introduces an advanced canvas attention scheduling method that allows variable canvas sizes and frame rates, significantly improving real-time perception accuracy on edge devices under strict resource constraints.
Contribution
It extends existing canvas attention scheduling by enabling variable canvas sizes and frame rates, enhancing perception performance on resource-limited edge platforms.
Findings
Higher mean average precision (mAP) achieved
Improved recall rates observed
Better quality/cost trade-offs demonstrated
Abstract
Real-time perception on edge platforms faces a core challenge: executing high-resolution object detection under stringent latency constraints on limited computing resources. Canvas-based attention scheduling was proposed in earlier work as a mechanism to reduce the resource demands of perception subsystems. It consolidates areas of interest in an input data frame onto a smaller area, called a canvas frame, that can be processed at the requisite frame rate. This paper extends prior canvas-based attention scheduling literature by (i) allowing for variable-size canvas frames and (ii) employing selectable canvas frame rates that may depart from the original data frame rate. We evaluate our solution by running YOLOv11, as the perception module, on an NVIDIA Jetson Orin Nano to inspect video frames from the Waymo Open Dataset. Our results show that the additional degrees of freedom improve…
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