An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA-Reference-Cited by-同舟云学术

An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA

Published:2024 Issue: Volume: Page:212-222
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Isik Murat,Oldland Matthew,Zhou Lifeng

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-47721-8_14

Reference20 articles.

1. Zhang, Y.: A better autoencoder for image: Convolutional autoencoder. ICONIP17-DCEC. Available online http://users.cecs.anu.edu.au/Tom.Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58.pdf. (Accessed on 23 March 2017). (2018)

2. Bank, D., Noam, K., Raja, G.: Autoencoders. arXiv preprint arXiv:2003.05991. (2020)

3. Badr, W.: Auto-encoder: What is it? And what is it used for? (Part 1). Medium, (2021). https://towardsdatascience.com/auto-encoder-what-is-it-and-what-is-it-used-for-part-1-3e5c6f017726

4. Dataman., Kuo, C.: Convolutional autoencoders for image noise reduction.Medium, (2022). towardsdatascience.com/convolutional-autoencoders-for-image-noise-reduction-32fce9fc1763

5. Zhang, C., et al.: Optimizing FPGA-based accelerator design for deep convolutional neural networks. In: Proceedings of the 2015 ACM/SIGDA international symposium on field-programmable gate arrays. (2015)

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