research-article

Compressive sensing based asymmetric semantic image compression for resource-constrained IoT system

Authors:

Jianghui Zhang,

Shu-Tao XiaAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 877 - 882

https://doi.org/10.1145/3489517.3530520

Published: 23 August 2022 Publication History

Abstract

The widespread application of Internet-of-Things (IoT) and deep learning have made machine-to-machine semantic communication possible. However, it remains challenging to deploy DNN model on IoT devices, due to their limited computing and storage capacity. In this paper, we propose Compressed Sensing based Asymmetric Semantic Image Compression (CS-ASIC) for resource-constrained IoT systems, which consists of a lightweight front encoder and a deep iterative decoder offloaded at the server. We further consider a task-oriented scenario and optimize CS-ASIC for the semantic recognition tasks. The experiment results demonstrate that CS-ASIC achieves considerable data-semantic rate-distortion trade-off, and low encoding complexity over prevailing codecs.

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Cited By

Chen BTang DHuang YAn BWang YWang X(2024)CMCL: Cross-Modal Compressive Learning for Resource-Constrained Intelligent IoT SystemsIEEE Internet of Things Journal10.1109/JIOT.2024.335977411:15(25534-25542)Online publication date: 1-Aug-2024
https://doi.org/10.1109/JIOT.2024.3359774

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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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New York, NY, United States

Publication History

Published: 23 August 2022

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National Natural Science Foundation of China
Guangdong Basic and Applied Basic Research Foundation
PCNL KEY project

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DAC '22

Sponsor:

SIGDA

DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Chen BTang DHuang YAn BWang YWang X(2024)CMCL: Cross-Modal Compressive Learning for Resource-Constrained Intelligent IoT SystemsIEEE Internet of Things Journal10.1109/JIOT.2024.335977411:15(25534-25542)Online publication date: 1-Aug-2024
https://doi.org/10.1109/JIOT.2024.3359774

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