Versatile Architectures of Artificial Neural Network with Variable Capacity-Reference-Cited by-同舟云学术

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Versatile Architectures of Artificial Neural Network with Variable Capacity

Published:2022-07-01 Issue:11 Volume:41 Page:6333-6353
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Basiri M. Mohamed Asan^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s00034-022-02087-3.pdf

Reference30 articles.

1. A. Ardakani, C. Condo, M. Ahmadi, W.J. Gross, An architecture to accelerate convolution in deep neural networks. IEEE Trans. Circuits Syst. I Regul. Pap. 65(4), 1349–1362 (2018). https://doi.org/10.1109/TCSI.2017.2757036

2. M. Arvandi, S. Wu, A. Sadeghian, On the use of recurrent neural networks to design symmetric ciphers. IEEE Comput. Intell. Mag. 3(2), 42–53 (2008). https://doi.org/10.1109/MCI.2008.919075

3. M.A.M. Basiri, Flexible adaptive FIR filter designs using LMS algorithm, in 23rd International Symposium on VLSI Design and Test (VDAT), Communications in Computer and Information Science. Springer 1066, 61–71 (2019). https://doi.org/10.1007/978-981-32-9767-8_6

4. M.A.M. Basiri, N.M. Sk, An efficient VLSI architecture for lifting based 1D/2D-discrete wavelet transform. Microprocess. Microsyst. 47, 404–418 (2016). https://doi.org/10.1016/j.micpro.2016.08.007

5. M.A.M. Basiri, N.M. Sk, An efficient hardware based higher radix floating point MAC design. ACM Trans. Design Autom. Electron. Syst. 20(1), 151–1525 (2014). https://doi.org/10.1145/2667224

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