@inproceedings{163a127ad3c64f02bf1d1a67bef7f9d6,
title = "Asymmetric Weight Pruning for Resource-Limited IoT Devices in Semantic Communications",
abstract = "In this paper, we explore the application of semantic communication in Internet of Things (IoT) networks, taking into account the limited computing resources of IoT devices. We propose a resource-efficient asymmetric autoencoder framework that leverages weight pruning and quantization techniques to compress the encoder model on IoT devices. Our simulation results demonstrate that the proposed encoder-only pruning model achieves up to 98\% model compression without any performance degradation. Furthermore, even with 8-bit quantization applied, the pruned model exhibits no performance loss.",
keywords = "IoT, model compression, semantic communication, weight pruning",
author = "Lee, \{Su Jin\} and Lee, \{Ye Hoon\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th International Conference on Information and Communication Technology Convergence, ICTC 2024 ; Conference date: 16-10-2024 Through 18-10-2024",
year = "2024",
doi = "10.1109/ICTC62082.2024.10826953",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "182--183",
booktitle = "ICTC 2024 - 15th International Conference on ICT Convergence",
}