Accuracy, Training Time and Hardware Efficiency Trade-Offs for Quantized Neural Networks on FPGAs-Reference-Cited by-同舟云学术

Accuracy, Training Time and Hardware Efficiency Trade-Offs for Quantized Neural Networks on FPGAs

Published:2020 Issue: Volume: Page:121-135
ISSN:0302-9743
Container-title:Applied Reconfigurable Computing. Architectures, Tools, and Applications
language:
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Author:

Bacchus Pascal,Stewart Robert,Komendantskaya Ekaterina

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-44534-8_10

Reference29 articles.

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3. Blott, M., et al.: FINN-R: an end-to-end deep-learning framework for fast exploration of quantized neural networks. Trans. Reconfigurable Technol. Syst. 11(3), 16:1–16:23 (2018)

4. Chen, T., et al.: DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning. In: ASPLOS 2014, Salt Lake City, UT, USA, 1–5 March 2014, pp. 269–284. ACM (2014)

5. Cheng, Y., Yu, F.X., Feris, R.S., Kumar, S., Choudhary, A.N., Chang, S.: Fast neural networks with circulant projections. CoRR abs/1502.03436 (2015)

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