Pse: mixed quantization framework of neural networks for efficient deployment
Author:
Funder
National Natural Science Foundation of China
Ningbo Natural Science Foundation project
Publisher
Springer Science and Business Media LLC
Subject
Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11554-023-01366-9.pdf
Reference62 articles.
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3. Chen, J., Bai, S., Huang, T., Wang, M., Tian, G., Liu, Y.: Data-free quantization via mixed-precision compensation without fine-tuning. Pattern Recognit. 143, 109780 (2023)
4. Chen, J., Liu, L., Liu, Y., Zeng, X.: A learning framework for n-bit quantized neural networks toward fpgas. IEEE Trans. Neural Netw. Learn. Syst. 32(3), 1067–1081 (2021). https://doi.org/10.1109/TNNLS.2020.2980041
5. Chen, T., Du, Z., Sun, N., Wang, J., Wu, C., Chen, Y., Temam, O.: Diannao: a small-footprint high-throughput accelerator for ubiquitous machine-learning. in: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (2014)
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