TinyML-CAM-Reference-Cited by-同舟云学术

TinyML-CAM

Published:2022-10-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
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Author:

Sudharsan Bharath¹,Salerno Simone²,Ranjan Rajiv³

Affiliation:

1. General Motors, Limerick, Ireland

2. Eloquent Arduino, Bari, Italy

3. Newcastle University, Newcastle, U.K

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3495243.3558264

Reference6 articles.

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

2. Eloquentarduino : https://github.com/eloquentarduino , 2022 . Eloquentarduino: https://github.com/eloquentarduino, 2022.

3. Everywhereml : https://pypi.org/project/everywhereml/0.0.1/ , 2022 . Everywhereml: https://pypi.org/project/everywhereml/0.0.1/, 2022.

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

5. C. Banbury , V. J. Reddi , Mlperf tiny benchmark . Neural Information Processing Systems Track on Datasets and Benchmarks , 2021 . C. Banbury, V. J. Reddi, et al. Mlperf tiny benchmark. Neural Information Processing Systems Track on Datasets and Benchmarks, 2021.

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1. StreamTinyNet: video streaming analysis with spatial-temporal TinyML;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System;Sensors;2023-12-30