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Using artificial neural networks for...

Rosa, Joao P. S.

Using artificial neural networks for analog integrated circuit design automation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using artificial neural networks for analog integrated circuit design automationby Joao P. S. Rosa ... [et al.].
other author:	Rosa, Joao P. S.
Published:	Cham :Springer international Publishing :2020.
Description:	xviii, 101 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Integrated circuitsComputer-aided design.
Online resource:	https://doi.org/10.1007/978-3-030-35743-6
ISBN:	9783030357436$q(electronic bk.)

Using artificial neural networks for analog integrated circuit design automation
Using artificial neural networks for analog integrated circuit design automation[electronic resource] /by Joao P. S. Rosa ... [et al.]. - Cham :Springer international Publishing :2020. - xviii, 101 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

introduction -- Related Work -- Overview of Artificial Neural Networks (ANNs) -- On the Exploration of Promising Analog iC Designs via ANNs -- ANNs as an Alternative for Automatic Analog iC Placement -- Conclusions.

This book addresses the automatic sizing and layout of analog integrated circuits (iCs) using deep learning (DL) and artificial neural networks (ANN) it explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. in opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices' sizes to circuits' performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog iC sizing as a direct map from specifications to the devices' sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit's performances as input features and devices' sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.

ISBN: 9783030357436$q(electronic bk.)

Standard No.: 10.1007/978-3-030-35743-6doiSubjects--Topical Terms:

225888
Integrated circuits
--Computer-aided design.

LC Class. No.: TK7874

Dewey Class. No.: 621.3815

Using artificial neural networks for analog integrated circuit design automation
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505 0 $a introduction -- Related Work -- Overview of Artificial Neural Networks (ANNs) -- On the Exploration of Promising Analog iC Designs via ANNs -- ANNs as an Alternative for Automatic Analog iC Placement -- Conclusions.
520 $a This book addresses the automatic sizing and layout of analog integrated circuits (iCs) using deep learning (DL) and artificial neural networks (ANN) it explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. in opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices' sizes to circuits' performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog iC sizing as a direct map from specifications to the devices' sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit's performances as input features and devices' sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.
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