IEICE TRANSACTIONS on Fundamentals
Compact Representations of Logic Functions Using Heterogeneous MDDs
Summary :
In this paper, we propose a compact representation of logic functions using Multi-valued Decision Diagrams (MDDs) called heterogeneous MDDs. In a heterogeneous MDD, each variable may take a different domain. By partitioning binary input variables and representing each partition as a single multi-valued variable, we can produce a heterogeneous MDD with 16% smaller memory size than a Reduced Ordered Binary Decision Diagram (ROBDD), and with comparable memory size to Free Binary Decision Diagrams (FBDDs). And also, heterogeneous MDDs have shorter Average Path Length (APL) than ROBDDs and FBDDs. We minimized a large number of benchmark functions to show the compactness of heterogeneous MDDs.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.12 pp.3168-3175
- Publication Date
- 2003/12/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
- Category
- Logic and High Level Synthesis
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Shinobu NAGAYAMA, Tsutomu SASAO, "Compact Representations of Logic Functions Using Heterogeneous MDDs" in IEICE TRANSACTIONS on Fundamentals,
vol. E86-A, no. 12, pp. 3168-3175, December 2003, doi: .
Abstract: In this paper, we propose a compact representation of logic functions using Multi-valued Decision Diagrams (MDDs) called heterogeneous MDDs. In a heterogeneous MDD, each variable may take a different domain. By partitioning binary input variables and representing each partition as a single multi-valued variable, we can produce a heterogeneous MDD with 16% smaller memory size than a Reduced Ordered Binary Decision Diagram (ROBDD), and with comparable memory size to Free Binary Decision Diagrams (FBDDs). And also, heterogeneous MDDs have shorter Average Path Length (APL) than ROBDDs and FBDDs. We minimized a large number of benchmark functions to show the compactness of heterogeneous MDDs.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_12_3168/_p
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@ARTICLE{e86-a_12_3168,
author={Shinobu NAGAYAMA, Tsutomu SASAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Compact Representations of Logic Functions Using Heterogeneous MDDs},
year={2003},
volume={E86-A},
number={12},
pages={3168-3175},
abstract={In this paper, we propose a compact representation of logic functions using Multi-valued Decision Diagrams (MDDs) called heterogeneous MDDs. In a heterogeneous MDD, each variable may take a different domain. By partitioning binary input variables and representing each partition as a single multi-valued variable, we can produce a heterogeneous MDD with 16% smaller memory size than a Reduced Ordered Binary Decision Diagram (ROBDD), and with comparable memory size to Free Binary Decision Diagrams (FBDDs). And also, heterogeneous MDDs have shorter Average Path Length (APL) than ROBDDs and FBDDs. We minimized a large number of benchmark functions to show the compactness of heterogeneous MDDs.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - Compact Representations of Logic Functions Using Heterogeneous MDDs
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3168
EP - 3175
AU - Shinobu NAGAYAMA
AU - Tsutomu SASAO
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2003
AB - In this paper, we propose a compact representation of logic functions using Multi-valued Decision Diagrams (MDDs) called heterogeneous MDDs. In a heterogeneous MDD, each variable may take a different domain. By partitioning binary input variables and representing each partition as a single multi-valued variable, we can produce a heterogeneous MDD with 16% smaller memory size than a Reduced Ordered Binary Decision Diagram (ROBDD), and with comparable memory size to Free Binary Decision Diagrams (FBDDs). And also, heterogeneous MDDs have shorter Average Path Length (APL) than ROBDDs and FBDDs. We minimized a large number of benchmark functions to show the compactness of heterogeneous MDDs.
ER -
English
