In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks

• URL: http://arxiv.org/abs/2210.03555v2
• Date: Sun, 2 Apr 2023 14:49:18 GMT
• Title: In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks
• Authors: Kaibin Huang, Hai Wu, Zhiyan Liu and Xiaojuan Qi
• Abstract summary: In-situ model downloading aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level. We propose a 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
• Score: 61.416494781759326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The sixth-generation (6G) mobile networks are expected to feature the ubiquitous deployment of machine learning and AI algorithms at the network edge. With rapid advancements in edge AI, the time has come to realize intelligence downloading onto edge devices (e.g., smartphones and sensors). To materialize this version, we propose a novel technology in this article, called in-situ model downloading, that aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. Its distinctive feature is the adaptation of downloading to time-varying situations (e.g., application, location, and time), devices' heterogeneous storage-and-computing capacities, and channel states. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level to support adaptive model downloading. We further propose a virtualized 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library. Furthermore, experiments are conducted to quantify 6G connectivity requirements and research opportunities pertaining to the proposed technology are discussed.

Related papers

• Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services [55.0337199834612]
  Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics.
  arXiv  Detail & Related papers  (2024-11-03T07:01:13Z)
• Profiling AI Models: Towards Efficient Computation Offloading in Heterogeneous Edge AI Systems [0.2357055571094446]
  We propose a research roadmap focused on profiling AI models, capturing data about model types and underlying hardware to predict resource utilisation and task completion time. Experiments with over 3,000 runs show promise in optimising resource allocation and enhancing Edge AI performance.
  arXiv  Detail & Related papers  (2024-10-30T16:07:14Z)
• Computer Vision Model Compression Techniques for Embedded Systems: A Survey [75.38606213726906]
  This paper covers the main model compression techniques applied for computer vision tasks. We present the characteristics of compression subareas, compare different approaches, and discuss how to choose the best technique. We also share codes to assist researchers and new practitioners in overcoming initial implementation challenges.
  arXiv  Detail & Related papers  (2024-08-15T16:41:55Z)
• Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration [56.330705072736166]
  We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
  arXiv  Detail & Related papers  (2023-10-26T15:19:40Z)
• Large Language Models Empowered Autonomous Edge AI for Connected Intelligence [51.269276328087855]
  Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
  arXiv  Detail & Related papers  (2023-07-06T05:16:55Z)
• Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
  Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenges.
  arXiv  Detail & Related papers  (2023-03-15T02:18:21Z)
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [39.223546118441476]
  6G will revolutionize the evolution of wireless from "connected things" to "connected intelligence" Deep learning and big data analytics based AI systems require tremendous computation and communication resources. edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence.
  arXiv  Detail & Related papers  (2021-11-24T11:47:16Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.