On the Effect of Quantization on Deep Neural Networks Performance

Author:

Tmamna Jihene,Fourati Rahma,Ltifi Hela

Publisher

Springer Nature Switzerland

Reference34 articles.

1. Bablani, D., Mckinstry, J.L., Esser, S.K., Appuswamy, R., Modha, D.S.: Efficient and effective methods for mixed precision neural network quantization for faster, energy-efficient inference. arXiv preprint arXiv:2301.13330 (2023)

2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

3. Bernhard, R., Moellic, P.A., Dutertre, J.M.: Impact of low-bitwidth quantization on the adversarial robustness for embedded neural networks. In: 2019 International Conference on Cyberworlds (CW), pp. 308–315. IEEE (2019)

4. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)

5. Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013)

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