MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network
Authors: 
Prianka Sengupta, Aakash Tyagi, Jiang Hu, Vivek K. Rajan, Hesham Mostafa, Somdeb MajumdarAuthors Info & Claims
Prianka Sengupta, Aakash Tyagi, Jiang Hu, Vivek K. Rajan, Hesham Mostafa, Somdeb MajumdarAuthors Info & ClaimsArticle No.: 24, Pages 1 - 8
Abstract
The initial steps of logic synthesis of digital designs involve finding minimized representations of Boolean logic functions. Existing optimization methods rely on iterative minimization operations that can result in a rapid increase in the runtime when the number of variables and terms of the Boolean functions increase. We propose a graph attention network (GAT) based logic minimization approach called MinBLoG, to narrow down the solution search space for Boolean functions. Our approach achieves more than 96% accuracy in identifying implicants that are a part of the minimized solution and ensures functional equivalency through correctness checking procedures. Experiments show that MinBLoG delivers minimization results for a wide range of Boolean functions significantly faster than well-known existing methods.
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Index Terms
- MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network
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Published In
September 2024
321 pages
ISBN:9798400706998
DOI:10.1145/3670474
- General Chairs:
- Hussam Amrouch,
- Jiang Hu,
- Program Chairs:
- Siddharth Garg,
- Yibo Lin
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.
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Published: 09 September 2024
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MLCAD '24: 2024 ACM/IEEE International Symposium on Machine Learning for CAD
September 9 - 11, 2024
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