# CSRAP: Enhanced Canvas Attention Scheduling for Real-Time Mission Critical Perception

arXiv: https://arxiv.org/abs/2508.04976 · HTML: https://mlsanity.com/paper/2508.04976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016)
- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework](https://mlsanity.com/paper/1706.01215/) (2017)
- [FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices](https://mlsanity.com/paper/1809.06970/) (2018)
- [Scheduling Real-time Deep Learning Services as Imprecise Computations](https://mlsanity.com/paper/2011.01112/) (2020)
- [VALO: A Versatile Anytime Framework for LiDAR-based Object Detection Deep Neural Networks](https://mlsanity.com/paper/2409.11542/) (2024)
- [CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2501.04982/) (2025)

## Abstract (arXiv)

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 the attainable quality/cost trade-offs, thereby allowing for a consistently higher mean average precision (mAP) and recall with respect to the state of the art.