More Efficient CMMs on FPGAs: Instantiated Ternary Adders for Computation Coding-Reference-Cited by-同舟云学术

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More Efficient CMMs on FPGAs: Instantiated Ternary Adders for Computation Coding

Published:2023 Issue: Volume: Page:275-289
ISSN:0302-9743
Container-title:Applied Reconfigurable Computing. Architectures, Tools, and Applications
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Author:

Lehnert Alexander^ORCID,Rosenberger Hans^ORCID,Müller Ralf^ORCID,Reichenbach Marc^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-42921-7_19

Reference22 articles.

1. Aksoy, L., Flores, P.M.: A novel method for the approximation of multiplierless constant matrix vector multiplication. Embedded Syst. 12 (2016). https://doi.org/10.1186/s13639-016-0033-y

2. Blott, M., et al.: FINN-R: an end-to-end deep-learning framework for fast exploration of quantized neural networks. ACM Trans. Reconfigurable Technol. Syst. 11(3) (2018). https://doi.org/10.1145/3242897

3. Demidovskij, A., Smirnov, E.: Effective post-training quantization of neural networks for inference on low power neural accelerator. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2020). https://doi.org/10.1109/IJCNN48605.2020.9207281

4. de Dinechin, F.: Reflections on 10 years of FloPoCo. In: 26th IEEE Symposium of Computer Arithmetic (ARITH-26) (2019)

5. Fan, A., et al.: Training with quantization noise for extreme model compression. arXiv preprint arXiv:2004.07320 (2020)

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