Automated placement of analog integrated circuits using priority-based constructive heuristic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00374569" target="_blank" >RIV/68407700:21730/24:00374569 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/24:00374569

Result on the web

<a href="https://doi.org/10.1016/j.cor.2024.106643" target="_blank" >https://doi.org/10.1016/j.cor.2024.106643</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cor.2024.106643" target="_blank" >10.1016/j.cor.2024.106643</a>

Result language

angličtina

Original language name

Automated placement of analog integrated circuits using priority-based constructive heuristic

Original language description

This paper presents a heuristic approach for solving the placement of Analog and Mixed-Signal Integrated Circuits. Placement is a crucial step in the physical design of integrated circuits. During this step, designers choose the position and variant of each circuit device. We focus on the specific class of analog placement, which requires so-called pockets, their possible merging, and parametrizable minimum distances between devices, which are features mostly omitted in recent research and literature. We formulate the problem using Integer Linear Programming and propose a priority-based constructive heuristic inspired by algorithms for the Facility Layout Problem. Our solution minimizes the perimeter of the circuit’s bounding box and the approximated wire length. Multiple variants of the devices with different dimensions are considered. Furthermore, we model constraints crucial for the placement problem, such as symmetry groups and blockage areas. Our outlined improvements make the heuristic suitable to handle complex rules of placement. With a search guided either by a Genetic Algorithm or a Covariance Matrix Adaptation Evolution Strategy, we show the quality of the proposed method on both synthetically generated and real-life industrial instances accompanied by manually created designs. Furthermore, we apply reinforcement learning to control the hyper-parameters of the genetic algorithm. Synthetic instances with more than 200 devices demonstrate that our method can tackle problems more complex than typical industry examples. We also compare our method with results achieved by contemporary state-of-the-art methods on the MCNC and GSRC datasets.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computer & Operations Research

ISSN

0305-0548

e-ISSN

1873-765X

Volume of the periodical

167

Issue of the periodical within the volume

July

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

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UT code for WoS article

001226059700001

EID of the result in the Scopus database

2-s2.0-85189641192