1.

Record Nr.

UNISALENTO991004067869707536

Autore

Saccoccio, Antonio

Titolo

Aliud pro alio consentiente creditore in solutum dare / Antonio Saccoccio

Pubbl/distr/stampa

Milano : Giuffrè, 2008

ISBN

9788814142932

Descrizione fisica

xi, 315 p. ; 24 cm

Collana

Collana del Dipartimento di scienze giuridiche dell'Università degli studi di Brescia

Soggetti

Datio in solutum

Creditori

Obbligazioni - Adempimento

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Con bibliografia e indice



2.

Record Nr.

UNINA9910366610703321

Autore

Rosa João P. S

Titolo

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation / / by João P. S. Rosa, Daniel J. D. Guerra, Nuno C. G. Horta, Ricardo M. F. Martins, Nuno C. C. Lourenço

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020

ISBN

3-030-35743-0

Edizione

[1st ed. 2020.]

Descrizione fisica

1 online resource (117 pages)

Collana

SpringerBriefs in Applied Sciences and Technology, , 2191-530X

Disciplina

621.3815

Soggetti

Electronic circuits

Signal processing

Image processing

Speech processing systems

Computational intelligence

Circuits and Systems

Signal, Image and Speech Processing

Computational Intelligence

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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. .

Sommario/riassunto

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. .