MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network
Year: 2024
Type: conference-paper
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.
Institutions: Texas A&M University, Intel (United States)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Funder: Universitas Brawijaya
Awards: G3784871731, G5721376053
OpenAlex ID: W4402172415