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New RAMS system adapts YOLOv8 tiers for edge AI perception

Researchers have developed RAMS, a novel runtime controller designed for embedded edge perception systems. RAMS dynamically switches between different tiers of YOLOv8 models based on real-time device resource monitoring and detection conditions. This adaptive approach aims to optimize the balance between inference latency and detection quality, particularly in resource-constrained environments like those found on Raspberry Pi 5 and NVIDIA Jetson Orin platforms. AI

RANK_REASON The cluster contains a research paper detailing a new system for edge AI perception. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kushal Khemani, Evan Leri, George Xu, Amit Hod ·

    RAMS: Resource-Adaptive and Detection-Conditioned Model Switching for Embedded Edge Perception

    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 sw…