Poster Abstract: Approach for Remote, On-Demand loading and Execution of TensorFlow Lite ML Models on Arduino IoT Boards-Reference-Cited by-同舟云学术

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Poster Abstract: Approach for Remote, On-Demand loading and Execution of TensorFlow Lite ML Models on Arduino IoT Boards

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
language:
Short-container-title:

Author:

Sudharsan Bharath¹,Salerno Simone²,Yadav Piyush³,Breslin John G.¹

Affiliation:

1. Data Science Institute, NUI,Galway,Ireland

2. Eloquent Arduino,Bari,Italy

3. Collins Aerospace ART,Cork,Ireland

Funder

SFI

European Regional Development Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9825912/9825917/09825918.pdf?arnumber=9825918

Reference9 articles.

1. TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers

2. An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware

3. A more secure and reliable ota update architecture for iot devices;lethaby;Texas Instruments,2018

4. Enabling Machine Learning on the Edge Using SRAM Conserving Efficient Neural Networks Execution Approach

Cited by 2 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. Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices;Electronics;2024-09-08

2. Applications of artificial intelligence technologies in water environments: From basic techniques to novel tiny machine learning systems;Process Safety and Environmental Protection;2023-12

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