Aussie AI

Trigonometric Approximations

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

Trigonometric Approximations

A very surprising method to replace multiplications with addition is to do so using trigonometric approximations. Note that this is not the same topic as “trigonometric neural networks” in other papers. I'm not sure that using sine and cosine instead of multiplication is going to catch on, but it sure is interesting.

Research papers on trigonometric approximation models:

  1. Jingyong Cai, Masashi Takemoto,Yuming Qiu andHironori Nakajo, 2021, Trigonometric Inference Providing Learning in Deep Neural Networks, Appl. Sci. 2021, 11(15), 6704; https://doi.org/10.3390/app11156704, https://www.mdpi.com/2076-3417/11/15/6704, PDF: https://www.mdpi.com/2076-3417/11/15/6704/pdf
  2. Jingyong Cai, 2022, Log-or-Trig: Towards efficient learning in deep neural networks, Thesis, Graduate School of Engineering, Tokyo University of Agriculture and Technology, https://tuat.repo.nii.ac.jp/?action=repository_action_common_download&item_id=1994&item_no=1&attribute_id=16&file_no=3, PDF: https://tuat.repo.nii.ac.jp/index.php?action=pages_view_main&active_action=repository_action_common_download&item_id=1994&item_no=1&attribute_id=16&file_no=1&page_id=13&block_id=39 (Examines LNS and trigonometric approximations.)

For more research papers on inefficient trigonometric approximations, see also https://www.aussieai.com/research/advanced-ai-mathematics#trig.

 

• Next:

• Up: Table of Contents

• Buy: Generative AI in C++: Coding Transformers and LLMs

Generative AI in C++ The new AI programming book by Aussie AI co-founders:
  • AI coding in C++
  • Transformer engine speedups
  • LLM models
  • Phone and desktop AI
  • Code examples
  • Research citations

Get your copy from Amazon: Generative AI in C++