EtinyNet: Extremely Tiny Network for TinyML

Author:

Xu Kunran,Li Yishi,Zhang Huawei,Lai Rui,Gu Lin

Abstract

There are many AI applications in high-income countries because their implementation depends on expensive GPU cards (~2000$) and reliable power supply (~200W). To deploy AI in resource-poor settings on cheaper (~20$) and low-power devices (

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. TinyAirNet: TinyML Model Transmission for Energy-Efficient Image Retrieval From IoT Devices;IEEE Communications Letters;2024-09

2. VRES-CNN: A Tiny Convolutional Image Classifier with Versatile Choice of Hyperparameters;2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2024-06-27

3. Cross-Modal Semantic Communications Over Wireless Emergency Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09

4. An Ultra-Low Power TinyML System for Real-Time Visual Processing at Edge;IEEE Transactions on Circuits and Systems II: Express Briefs;2023-07

5. A Comprehensive Survey on TinyML;IEEE Access;2023

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