We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments. The framework orchestrates the architecture search process on a server-side NAS service, enabling edge services to derive personalised architectures under device-level energy, computation, and memory constraints.
arXiv:2606. 29518v1 Announce Type: cross Abstract: With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge.
By Yihan Wang, Huiru Yan, Luxin Zhang, Long Cheng, Weiwei Chen, Ying Wang, Lei Zhang, Cheng Liu, Huawei Li
With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demands.
arXiv:2606. 27841v1 Announce Type: cross Abstract: The widespread adoption of Artificial Intelligence (AI) has led to increasing concerns about energy consumption, yet there is a lack of standardized methodologies to accurately estimate AI inference energy consumption, particularly across various tasks and architectures.
By Adrien Sardi, Marie-Line Alberi Morel, Sara Alouf, Fr\'ed\'eric Giroire, Joanna Moulierac
arXiv:2607. 16297v1 Announce Type: cross Abstract: Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications.
By Shuo Huai, Hao Kong, Xiangzhong Luo, Di Liu, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu
arXiv:2607. 22583v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved widespread adoption because of their strong reasoning and query-response capabilities.
By Muhammad Junaid Ali, Smail Niar, El-Ghazali Talbi
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
By Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng, Chrysa Papagianni
arXiv:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.
By Tyler King, Timothee Leleu
arXiv:2509. 23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.
By Mingyi Luo, Ruichen Zhang, Xiangwang Hou, Jun Du, Chunxiao Jiang, Yong Ren, Shiwen Mao
arXiv:2607. 09084v1 Announce Type: new Abstract: The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.
By Linhui Xiao, Guiping Cao, Mingyue Guo, Xianchao Guan, Fan Yang, Ming Tao, Xin Li, Yuxin Peng, Yaowei Wang
arXiv:2603. 18540v2 Announce Type: replace Abstract: The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices.
By Zheng Lin, Ons Aouedi, Zihan Fang, Wei Ni, Yue Gao, Symeon Chatzinotas, Xianhao Chen
arXiv:2607. 20162v1 Announce Type: new Abstract: The growing demand for intelligent processing at the edge of IoT networks is constrained by the severe computational and memory limitations of microcontroller units, which render impractical conventional deep learning approaches.
By Stefano Radice, Ludovico Casaccia, Riccaro Emanuele Beccalli, Bruno Paroli, Paolo Milani