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10-Bit Quantization (INT10)

  • Book Excerpt from "Generative AI in C++"
  • by David Spuler, Ph.D.

10-Bit Quantization (INT10)

Research papers on 10-bit quantization:

  1. M Giacobbe, TA Henzinger, M Lechner, 2020, How many bits does it take to quantize your neural network?, TACAS 2020, https://link.springer.com/chapter/10.1007/978-3-030-45237-7_5, PDF: https://link.springer.com/content/pdf/10.1007/978-3-030-45237-7_5.pdf (Ran experiments from 6-bit to 10-bit quantization.)
  2. J Shi, M Lu, F Chen, S Pu, Z Ma, 2022, Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression, arXiv preprint arXiv:2211.02854, https://arxiv.org/abs/2211.02854
  3. Javier Fernandez-Marques, Paul N. Whatmough, Andrew Mundy, Matthew Mattina, 2020, Searching for winograd-aware quantized networks, Proceedings of the 3rd MLSys Conference, Austin, TX, USA, 2020. PDF: https://proceedings.mlsys.org/paper_files/paper/2020/file/678e209691cd37f145a5502695378bac-Paper.pdf (Evaluates INT8, INT10, and INT16 quantization.)
  4. Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling, 2019, Data-free quantization through weight equalization and bias correction, PDF: https://openaccess.thecvf.com/content_ICCV_2019/papers/Nagel_Data-Free_Quantization_Through_Weight_Equalization_and_Bias_Correction_ICCV_2019_paper.pdf (Evaluates INT5, INT6, INT8, INT10, INT12, and INT16.)

See more papers on 10-bit quantization (INT10) at: https://www.aussieai.com/research/quantization#int10

 

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