Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment

• URL: http://arxiv.org/abs/2412.00862v1
• Date: Sun, 01 Dec 2024 15:52:05 GMT
• Title: Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment
• Authors: Songjie Xie, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief,
• Abstract summary: Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems. Real-time applications face practical challenges, such as incomplete coverage and potential malfunctions of edge servers. This study introduces a novel framework that utilizes shared anchor data across diverse systems.
• Score: 23.796344455232227
• License:
• Abstract: Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information. However, real-time applications face practical challenges, such as incomplete coverage and potential malfunctions of edge servers. This situation necessitates cross-model communication between different inference systems, enabling edge devices from one service provider to collaborate effectively with edge servers from another. Independent optimization of diverse edge systems often leads to incoherent feature spaces, which hinders the cross-model inference for existing task-oriented communication. To facilitate and achieve effective cross-model task-oriented communication, this study introduces a novel framework that utilizes shared anchor data across diverse systems. This approach addresses the challenge of feature alignment in both server-based and on-device scenarios. In particular, by leveraging the linear invariance of visual features, we propose efficient server-based feature alignment techniques to estimate linear transformations using encoded anchor data features. For on-device alignment, we exploit the angle-preserving nature of visual features and propose to encode relative representations with anchor data to streamline cross-model communication without additional alignment procedures during the inference. The experimental results on computer vision benchmarks demonstrate the superior performance of the proposed feature alignment approaches in cross-model task-oriented communications. The runtime and computation overhead analysis further confirm the effectiveness of the proposed feature alignment approaches in real-time applications.

Related papers

• Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation [51.53221300103261]
  This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture. A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions. Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection.
  arXiv  Detail & Related papers  (2025-02-12T09:01:25Z)
• RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning [15.616652008152098]
  This paper introduces a novel framework that achieves high accuracy and efficient matching while ensuring robustness in handling diverse scenarios. To improve efficiency, we begin by modeling the online map matching problem as an Online Markov Decision Process. Next, to enhance the model's robustness, we design a reinforcement learning method, enabling robust handling of real-time data.
  arXiv  Detail & Related papers  (2025-02-05T11:26:32Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Task-Oriented Over-the-Air Computation for Multi-Device Edge AI [57.50247872182593]
  6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system.
  arXiv  Detail & Related papers  (2022-11-02T16:35:14Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Task-Oriented Communication for Multi-Device Cooperative Edge Inference [14.249444124834719]
  cooperative edge inference can overcome the limited sensing capability of a single device, but it substantially increases the communication overhead and may incur excessive latency. We propose a learning-based communication scheme that optimize local feature extraction and distributed feature encoding in a task-oriented manner.
  arXiv  Detail & Related papers  (2021-09-01T03:56:20Z)
• Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach [3.983055670167878]
  A low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimize feature extraction, source coding, and channel coding.
  arXiv  Detail & Related papers  (2021-02-08T12:53:32Z)
• Weakly supervised cross-domain alignment with optimal transport [102.8572398001639]
  Cross-domain alignment between image objects and text sequences is key to many visual-language tasks. This paper investigates a novel approach for the identification and optimization of fine-grained semantic similarities between image and text entities.
  arXiv  Detail & Related papers  (2020-08-14T22:48:36Z)

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.