Adaptive Semantic Token Selection for AI-native Goal-oriented Communications

• URL: http://arxiv.org/abs/2405.02330v1
• Date: Thu, 25 Apr 2024 13:49:50 GMT
• Title: Adaptive Semantic Token Selection for AI-native Goal-oriented Communications
• Authors: Alessio Devoto, Simone Petruzzi, Jary Pomponi, Paolo Di Lorenzo, Simone Scardapane,
• Abstract summary: We propose a novel design for AI-native goal-oriented communications. We exploit transformer neural networks under dynamic inference constraints on bandwidth and computation. We show that our model improves over state-of-the-art token selection mechanisms.
• Score: 11.92172357956248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation. Transformers have become the standard architecture for pretraining large-scale vision and text models, and preliminary results have shown promising performance also in deep joint source-channel coding (JSCC). Here, we consider a dynamic model where communication happens over a channel with variable latency and bandwidth constraints. Leveraging recent works on conditional computation, we exploit the structure of the transformer blocks and the multihead attention operator to design a trainable semantic token selection mechanism that learns to select relevant tokens (e.g., image patches) from the input signal. This is done dynamically, on a per-input basis, with a rate that can be chosen as an additional input by the user. We show that our model improves over state-of-the-art token selection mechanisms, exhibiting high accuracy for a wide range of latency and bandwidth constraints, without the need for deploying multiple architectures tailored to each constraint. Last, but not least, the proposed token selection mechanism helps extract powerful semantics that are easy to understand and explain, paving the way for interpretable-by-design models for the next generation of AI-native communication systems.

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)
• Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers [11.075247758198762]
  This paper introduces FUTURIST, a method for multimodal future semantic prediction that uses a unified and efficient visual sequence transformer architecture. We propose a VAE-free hierarchical tokenization process, which reduces computational complexity, streamlines the training pipeline, and enables end-to-end training with high-resolution, multimodal inputs. We validate FUTURIST on the Cityscapes dataset, demonstrating state-of-the-art performance in future semantic segmentation for both short- and mid-term forecasting.
  arXiv  Detail & Related papers  (2025-01-14T18:34:14Z)
• Vision Transformer-based Semantic Communications With Importance-Aware Quantization [13.328970689723096]
  This paper presents a vision transformer (ViT)-based semantic communication system with importance-aware quantization (IAQ) for wireless image transmission. We show that our IAQ framework outperforms conventional image compression methods in both error-free and realistic communication scenarios.
  arXiv  Detail & Related papers  (2024-12-08T19:24:47Z)
• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data [23.597759255020296]
  We propose a distributed multi-modal semantic communication framework incorporating the conventional channel encoder/decoder. We establish a general rate-adaptive coding mechanism for various types of multi-modal semantic tasks. Numerical results show that the proposed mechanism fares better than both conventional communication and existing semantic communication systems.
  arXiv  Detail & Related papers  (2023-05-18T07:31:37Z)
• 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)
• Transformers Solve the Limited Receptive Field for Monocular Depth Prediction [82.90445525977904]
  We propose TransDepth, an architecture which benefits from both convolutional neural networks and transformers. This is the first paper which applies transformers into pixel-wise prediction problems involving continuous labels.
  arXiv  Detail & Related papers  (2021-03-22T18:00:13Z)
• 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)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

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.