A Practical View on Training Neural Networks in the Edge

Author:

Rüb Marcus,Sikora Prof. Dr. Axel

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference59 articles.

1. AbdulQader, D., Krishnan, S., and Coelho, JR, C.N. (25.03.2021). Enabling incremental training with forward pass for edge devices. URL https://arxiv.org/pdf/2103.14007.

2. Aljundi, R., Rohrbach, M., and Tuytelaars, T. (14.06.2018). Selfless sequential learning. URL https://arxiv.org/pdf/1806.05421.

3. Arm Ltd. (06.06.2021). Arm nn sdk. URL https://www.arm.com/products/silicon-ip-cpu/ethos/arm-nn.

4. Belouadah, E., Popescu, A., and Kanellos, I. (03.11.2020). A comprehensive study of class incremental learning algorithms for visual tasks. URL https://arxiv.org/pdf/2011.01844.

5. Cai, H., Chen, T., Zhang, W., Yu, Y., and Wang, J. (16.07.2017). Efficient architecture search by network transformation. URL https://arxiv.org/pdf/1707.04873.

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1. Advancing On-Device Neural Network Training with TinyPropv2: Dynamic, Sparse, and Efficient Backpropagation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption;2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS);2024-05-12

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