2003•Unpublished venueRequires access

A Boolean extraction technique for multiple-level logic optimization

Oh-Hyeong Kwon

Open publisher page 6 citations

Abstract

Extraction is the most important step in global minimization. Its approach is to identify and extract subexpressions, which are multiple-cubes or single-cubes, common to two or more expressions which can be used to reduce the total number of literals in a Boolean network. Extraction is described as either algebraic or Boolean, according to the trade-off between run-time and optimization. Boolean extraction is capable of providing better results, but difficulty in finding common Boolean divisors arises. In this paper, we present a new method for Boolean extraction to remove the difficulty. The key idea is to identify and extract two-cube Boolean subexpression pairs from each expression in a Boolean network. Experimental results show improvements in literal counts over the extraction in SIS for some benchmark circuits.

About this research paper

What this paper is about

Extraction is the most important step in global minimization. Its approach is to identify and extract subexpressions, which are multiple-cubes or single-cubes, common to two or more expressions which can be used to reduce the total number of literals in a Boolean network. Extraction is described as either algebraic or Boolean, according to the trade-off between run-time and optimization. Boolean extraction is capable of providing better results, but difficulty in finding common Boolean divisors arises. In this paper, we present a new method for Boolean extraction to remove the difficulty. The key idea is to identify and extract two-cube Boolean subexpression pairs from each expression in a Boolean network. Experimental results show improvements in literal counts over the extraction in SIS for some benchmark circuits.

Why it matters

OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Extraction is the most important step in global minimization. Its approach is to identify and extract subexpressions, which are multiple-cubes or single-cubes, common to two or more expressions which can be used to reduce the total number of literals in a Boolean network. Extraction is described as either algebraic or Boolean, according to the trade-off between run-time and optimization. Boolean extraction is capable of providing better results, but difficulty in finding common Boolean divisors arises. In this paper, we present a new method for Boolean extraction to remove the difficulty. The key idea is to identify and extract two-cube Boolean subexpression pairs from each expression in a Boolean network. Experimental results show improvements in literal counts over the extraction in SIS for some benchmark circuits.

Key concepts: Boolean expression, Product term, Boolean circuit, Circuit minimization for Boolean functions, Maximum satisfiability problem, And-inverter graph, Standard Boolean model, Computer science

Related papers

Back to paper searchBrowse research topicsOriginal source
A Boolean extraction technique for multiple-level logic optimization — Research Paper | ScholarLens