RAMS: Resource-Adaptive and Detection-Conditioned Model Switching for Embedded Edge Perception
📰 ArXiv cs.AI
Learn how to implement RAMS, a resource-adaptive model switching system for edge perception, to balance inference latency and detection quality on embedded hardware
Action Steps
- Implement a runtime controller to monitor device resource pressure
- Calibrate switching thresholds based on idle behavior
- Define switching policies to select among multiple model tiers
- Integrate YOLOv8 models with different input sizes (e.g., 320/416/640 px)
- Test and evaluate the performance of RAMS on embedded hardware
Who Needs to Know This
Computer vision engineers and embedded systems developers can benefit from this technique to optimize object detection models for edge devices, improving performance and efficiency
Key Insight
💡 Dynamic model switching can significantly improve the performance of edge perception systems on embedded hardware
Share This
🚀 Improve edge perception with RAMS: a resource-adaptive model switching system for balancing latency and detection quality #EdgeAI #ComputerVision
Key Takeaways
Learn how to implement RAMS, a resource-adaptive model switching system for edge perception, to balance inference latency and detection quality on embedded hardware
Full Article
Title: RAMS: Resource-Adaptive and Detection-Conditioned Model Switching for Embedded Edge Perception
Abstract:
arXiv:2606.14716v1 Announce Type: cross Abstract: Edge object detection on embedded hardware requires balancing inference latency and detection quality under changing resource pressure. We present RAMS, a lightweight runtime controller that monitors device pressure, calibrates switching thresholds from idle behavior, and dynamically selects among three resident YOLOv8 tiers (NANO/SMALL/MEDIUM at 320/416/640 px) without model-reload latency. RAMS defines five switching policies, including two det
Abstract:
arXiv:2606.14716v1 Announce Type: cross Abstract: Edge object detection on embedded hardware requires balancing inference latency and detection quality under changing resource pressure. We present RAMS, a lightweight runtime controller that monitors device pressure, calibrates switching thresholds from idle behavior, and dynamically selects among three resident YOLOv8 tiers (NANO/SMALL/MEDIUM at 320/416/640 px) without model-reload latency. RAMS defines five switching policies, including two det
DeepCamp AI