arXiv Computer Vision
Thermal-Headroom Routing for Efficient Robot Perception in Extreme Heat
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arXiv Machine Learning
Sep 7
Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardware
The paper presents an empirically calibrated, state‑aware Dynamic Voltage and Frequency Scaling (DVFS) scheduler that eliminates thermal throttling on a passively cooled Raspberry Pi 5 during sustained YOLOv8n inference. By using time‑domain guards, absolute temperature bounds, and derivative triggers, the scheduler outperforms a temperature‑only baseline with a 6.8% higher frame rate and 1.9% less energy per frame, and it surpasses an actively cooled reference in energy efficiency. The study also identifies that the passive operating envelope closes at ambient temperatures above 27 °C, where nonlinear leakage undermines DVFS control, and demonstrates that correct scheduling can make mechanical cooling unnecessary within the mapped envelope.
arXiv AI
1d ago
Energy-Aware Open-Vocabulary Detection Scheduling for Low-Power Robotic Autonomy
arXiv:2611.07663v1 Announce Type: cross Abstract: Open-vocabulary object detection (OVD) lets robots search for run-time object queries, but fixed-rate inference spends embedded compute on low-value...
arXiv Machine Learning
Jun 24
EnerInfer: Energy-Aware On-Device LLM Inference
arXiv:2606. 23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck.
arXiv AI
Sep 23
LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning
arXiv:2609.23974v1 Announce Type: new Abstract: Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through vis...
arXiv Machine Learning
Sep 29
HybridInfer: Thermal-Aware Reinforcement-Learning Tier Routing for On-Device, Edge, and Cloud LLM Inference
HybridInfer is a thermal‑aware reinforcement‑learning router that selects among on‑device, edge, and cloud large language model tiers based on a phone’s thermal headroom and query complexity. Trained offline with a Q‑learning policy, it balances quality, latency, cost, and a thermal penalty, and includes a locality bonus that encourages on‑device execution. In real Android tests on 210 prompts, the learned router outperformed two hand‑tuned heuristics in quality while maintaining the lowest cost, and it proved more reliable and faster than always‑on‑device inference, especially for long queries.
Hugging Face Trending Papers
Sep 21
LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning
Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through visual perception. A representative example is Human...