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.
By Aayush Marasini, Zhaoxian Zhou
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...
By Abdul Basit, Muhammad Shafique
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.
By Bohua Zou, Nian Liu, Binqi Sun, Matteo Mascherin, Debayan Roy, Yutao Liu, Yu Peng, Ning Jia, Haibo Chen
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...
By Boxun Hu, Jiawei Ge, Axel Krieger, Peng Wang, Tinoosh Mohsenin
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.
By Simran Koul
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...
arXiv:2609.23695v1 Announce Type: new
Abstract: Recent advances in Physical AI have accelerated the use of foundation models in autonomous systems such as unmanned aerial vehicles (UAVs), which must...
By Mohamed Amine Ferrag, Merouane Debbah, Abderrahmane Lakas, Manu Perumkunnil, Norbert Tihanyi
arXiv:2608. 15502v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising foundation for Embodied AI, but their high inference cost poses significant challenges for deployment in robotic systems.
By Ao Zhou, Bo Dai, Le Yu, Xingyu Liu, Zeyu Hao, Lingkun Long, Chunming Hu, Jianlei Yang
arXiv:2609.09662v1 Announce Type: cross
Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pr...
By Weisi Yang, Stephen Xia
The paper introduces an AI-driven framework for thermal‑aware capacity planning in data centers, enabling temperature predictions in milliseconds by learning from key parameters such as rack power, server placement, and HVAC settings. The model is validated against high‑fidelity CFD simulations and achieves a 10,000‑fold speedup while maintaining high accuracy for unseen designs. This rapid prediction capability allows designers and operators to instantly optimize workload distribution and cooling efficiency.
By Yixing Li, Mark Fenton, Matthew Kaufeler, Ka Ming Leung, Xin Ai, Zhiyu Zeng
arXiv:2601. 06425v2 Announce Type: replace-cross Abstract: Leakage power in multicore embedded systems now rivals dynamic power, so DVFS schedulers must respect deadlines and thermal limits, not just average makespan.
By Mohammad Pivezhandi, Abusayeed Saifullah, Ali Jannesari
arXiv:2603. 23640v2 Announce Type: replace-cross Abstract: Deploying large language models on-device for always-on personal agents demands sustained inference from hardware tightly constrained in power, thermal envelope, and memory.
By Pranay Tummalapalli, Sahil Arayakandy, Ritam Pal, Kautuk Kundan