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conference paper

Structural Rewriting in XOR-Majority Graphs

Chu, Zhufei
•
Soeken, Mathias  
•
Xia, Yinshui
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January 1, 2019
24Th Asia And South Pacific Design Automation Conference (Asp-Dac 2019)
24th Asia and South Pacific Design Automation Conference (ASP-DAC)

In this paper, we present a structural rewriting method for a recently proposed XOR-Majority graph (XMG), which has exclusive-OR (XOR), majority-of-three (MAJ), and inverters as primitives. XMGs are an extension of Majority-Inverter Graphs (MIGs). Previous work presented an axiomatic system, Omega, and its derived transformation rules for manipulation of MIGs. By additionally introducing XOR primitive, the identities of MAJ-XOR operations should be exploited to enable powerful logic rewriting in XMGs. We first proposed two MAJ-XOR identities and exploit its potential optimization opportunities during structural rewriting. Then, we discuss the rewriting rules that can be used for different operations. Finally, we also address structural XOR detection problem in MIG. The experimental results on EPFL benchmark suites show that the proposed method can optimize the size/depth product of XMGs and its mapped look-up tables (LUTs), which in turn benefits the quantum circuit synthesis that using XMG as the underlying logic representations.

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Type
conference paper
DOI
10.1145/3287624.3287671
Web of Science ID

WOS:000507459700115

Author(s)
Chu, Zhufei
Soeken, Mathias  
Xia, Yinshui
Wang, Lunyao
De Micheli, Giovanni  
Date Issued

2019-01-01

Publisher

ASSOC COMPUTING MACHINERY

Publisher place

New York

Published in
24Th Asia And South Pacific Design Automation Conference (Asp-Dac 2019)
ISBN of the book

978-1-4503-6007-4

Start page

663

End page

668

Subjects

logic synthesis

•

logic networks

•

rewriting

•

majority logic

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSI1  
LSI2  
Event nameEvent placeEvent date
24th Asia and South Pacific Design Automation Conference (ASP-DAC)

Tokyo, JAPAN

Jan 21-24, 2019

Available on Infoscience
January 31, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/165038
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