An Empirical Study of Convolutional Neural Network Compressions within Low-Power Devices

Author:

Garbay Thomas1,Hocine Karim1,Hachicha Khalil1,Pinna Andrea1,Granado Bertrand1

Affiliation:

1. Sorbonne Université CNRS, LIP6,Paris,France,F-75005

Publisher

IEEE

Reference14 articles.

1. TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems;David

2. Benchmarking TinyML Systems: Challenges and Direction;Banbury;arXiv:2003.04821 [cs],2021

3. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

4. An Empirical Study of Low Precision Quantization for TinyML;Zhuo;arXiv:2203.05492 [cs],2022

5. BinaryConnect: Training Deep Neural Networks with binary weights during propagations;Courbariaux;Advances in Neural Information Processing Systems,2015

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