[24] Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. Dorefa-net: Training low bandwidth convolutional neural networks with low bandwidth gradients. arXiv preprint arXiv:1606.06160, 2016.
[25] Sangyun Oh, Hyeonuk Sim, Sugil Lee, and Jongeun Lee. Automated log-scale quantization for low-cost deep neural networks. In CVPR, pages 742-751, 2021.
[26] Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, and Kevin Bailly. Spiq: Data-free per-channel static input quantization. WACV, 2023.
[27] Yuhang Li, Feng Zhu, Ruihao Gong, Mingzhu Shen, Xin Dong, Fengwei Yu, Shaoqing Lu, and Shi Gu. Mixmix: All you need for data-free compression are feature and data mixing. In ICCV, pages 4410-4419, 2021.
[28] Boyuan Feng, Yuke Wang, Tong Geng, Ang Li, and Yufei Ding. Apnn-tc: Accelerating arbitrary precision neural networks on amperegpu tensor cores. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–13, 2021.
[29] Cliff Robinson. Untether.ai boqueria 1458 risc-v core ai accelerator, Aug 2022.
[30] Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer. A survey of quantization methods for efficient neural network inference. arXiv preprint arXiv:2103.13630, 2021.
[31] Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440, 2016.
[32] Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, and Kevin Bailly. Red: Looking for redundancies for data-free structured compression of deep neural networks. NeurIPS, 2021.
[33] J. Deng, W. Dong, et al. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2009.
[34] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html, 2012.
[35] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In CVPR, pages 3213-3223, 2016.
[36] Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 353-355. Association for Computational Linguistics, 2018.
[37] Edouard Yvinec, Arnaud Dapogny, and Kevin Bailly. To fold or not to fold: a necessary and sufficient condition on batch-normalization layers folding. *IJCAI*, 2022.
[38] Intel. Intel® distribution of openvino™ toolkit. Intel, 2022.
[39] Nvidia. Nvidia distribution of tensorsr toolkit. Nvidia, 2022.
[40] Shoukai Xu, Haokun Li, Bohan Zhuang, Jing Liu, Jiezhang Cao, Chuangrun Liang, and Mingkui Tan. Generative low-bitwidth data free quantization. In ECCV, pages 1-17. Springer, 2020.
[41] Daisuke Miyashita, Edward H Lee, and Boris Murmann. Convolutional neural networks using logarithmic data representation. arXiv preprint arXiv:1603.01025, 2016.
[42] Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068, 2022.
[43] Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. LIm. int8(): 8-bit matrix multiplication for transformers at scale. arXiv preprint arXiv:2208.07339, 2022.
[44] Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, and Kevin Bailly. Powerquant: Automorphism search for non-uniform quantization. In ICLR, 2023.
[45] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. BMVC 2014, 2014.
[46] Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort. Up or down? adaptive rounding for post-training quantization. In ICML, pages 7197-7206. PMLR, 2020.
[47] Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Brecq: Pushing the limit of post-training quantization by block reconstruction. NeurIPS, 2021.
[48] Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044, 2019.