.png){kind=icon}
返回
Digital Semantic Device-Edge Co-Inference With Task-Oriented ARQ
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2024.3390213.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper considers a device-edge co-inference system for image classification tasks. The system splits a deep learning model between an edge device (ED) and an edge server (ES), where the ED feeds raw images to its local model and transmits the output semantic features to the ES for classification. We address two key challenges in transmitting continuous features through modern digital wireless communication systems. First, we devise a smooth sine-based surrogate function to approximate the non-differentiable quantization computation applied to the continuous feature vector during model training. This approach results in stable gradient backpropagation and efficient training convergence. Second, we propose a novel task-oriented automatic repeat request (ARQ) mechanism, namely TARQ, to overcome communication error. Compared to the conventional bit-error detection based ARQ, TARQ makes retransmission decisions based on the current SNR (Signal-to-Noise Ratio) and inference result, effectively reducing the retransmission probability without degrading the inference accuracy. Simulation results show that the proposed scheme enjoys fast training convergence and attains an inference accuracy only 0.62% lower than the ideal performance. Meanwhile, it reduces the inference delay of conventional ARQ scheme by 25.5% on average, achieving an efficient tradeoff between inference accuracy and delay.
Keyword:
Task analysis
Semantics
Training
Feature extraction
Quantization (signal)
Artificial intelligence
Vectors
Digital semantic communication
device-edge co-inference
task-oriented ARQ
image classification
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
Palladium-catalyzed substitution of esters of naphthylmethanols, 1-naphthylethanols, and analogues by sodium dimethyl malonate. Stereoselective synthesis from enantiomerically pure substrates.png)
Tetrahedron
TetrahedronIF0
PREAI
分享收藏Task Partitioning and Offloading in DNN-Task Enabled Mobile Edge Computing Networks支持DNN任务的移动边缘计算网络中的任务划分和卸载
PREAI
分享收藏分享收藏
查看更多内容