Proposal for a Generalized Convolution to Mitigate Heat Generation in Convolutional Neural Networks
DOI:
https://doi.org/10.29020/nybg.ejpam.v18i1.5707Keywords:
heat generation, convolutional neural networks, geralized convolutionAbstract
In convolutional neural networks (CNN), the problem of heat generation is becoming a significant issue. This challenge can be mitigated through both hardware and software methods. This study focuses on drastically reducing the amount of computations and, consequently, the heat generation. Specifically, the proposed approach reduces computations by approximately $m \times 2^n$, where $m$ is the number of layers and $n$ is the size of the node.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Hwajoon Kim, Byeongjae Kang, Sunyoung Yeun, Eunyoung Lim
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Upon acceptance of an article by the European Journal of Pure and Applied Mathematics, the author(s) retain the copyright to the article. However, by submitting your work, you agree that the article will be published under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This license allows others to copy, distribute, and adapt your work, provided proper attribution is given to the original author(s) and source. However, the work cannot be used for commercial purposes.
By agreeing to this statement, you acknowledge that:
- You retain full copyright over your work.
- The European Journal of Pure and Applied Mathematics will publish your work under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
- This license allows others to use and share your work for non-commercial purposes, provided they give appropriate credit to the original author(s) and source.