Ricardo M.F. Martins's Books

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

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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

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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Page Count

117

Publisher

Springer Nature

Published Date

ISBN 10

3030357430

ISBN 13

9783030357436

Generating Analog IC Layouts with LAYGEN II

By: Ricardo M. F. Martins,Nuno C. C. Lourenço,Nuno C.G. Horta

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Generating Analog IC Layouts with LAYGEN II

By: Ricardo M. F. Martins,Nuno C. C. Lourenço,Nuno C.G. Horta

This book presents an innovative methodology for the automatic generation of analog integrated circuits (ICs) layout, based on template descriptions and on evolutionary computational techniques. A design automation tool, LAYGEN II was implemented to validate the proposed approach giving special emphasis to reusability of expert design knowledge and to efficiency on retargeting operations.

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Page Count

104

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3642331467

ISBN 13

9783642331466

Speeding-Up Radio-Frequency Integrated Circuit Sizing with Neural Networks

By: João L. C. P. Domingues,Pedro J. C. D. C. Vaz,António P. L. Gusmão,Nuno C. G. Horta,Nuno C. C. Lourenço,Ricardo M. F. Martins

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Speeding-Up Radio-Frequency Integrated Circuit Sizing with Neural Networks

By: João L. C. P. Domingues,Pedro J. C. D. C. Vaz,António P. L. Gusmão,Nuno C. G. Horta,Nuno C. C. Lourenço,Ricardo M. F. Martins

In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the sizing task of RF IC design, which is used in two different steps of the automatic design process. The advances in telecommunications, such as the 5th generation broadband or 5G for short, open doors to advances in areas such as health care, education, resource management, transportation, agriculture and many other areas. Consequently, there is high pressure in today’s market for significant communication rates, extensive bandwidths and ultralow-power consumption. This is where radiofrequency (RF) integrated circuits (ICs) come in hand, playing a crucial role. This demand stresses out the problem which resides in the remarkable difficulty of RF IC design in deep nanometric integration technologies due to their high complexity and stringent performances. Given the economic pressure for high quality yet cheap electronics and challenging time-to-market constraints, there is an urgent need for electronic design automation (EDA) tools to increase the RF designers’ productivity and improve the quality of resulting ICs. In the last years, the automatic sizing of RF IC blocks in deep nanometer technologies has moved toward process, voltage and temperature (PVT)-inclusive optimizations to ensure their robustness. Each sizing solution is exhaustively simulated in a set of PVT corners, thus pushing modern workstations’ capabilities to their limits. Standard ANNs applications usually exploit the model’s capability of describing a complex, harder to describe, relation between input and target data. For that purpose, ANNs are a mechanism to bypass the process of describing the complex underlying relations between data by feeding it a significant number of previously acquired input/output data pairs that the model attempts to copy. Here, and firstly, the ANNs disrupt from the most recent trials of replacing the simulator in the simulation-based sizing with a machine/deep learning model, by proposing two different ANNs, the first classifies the convergence of the circuit for nominal and PVT corners, and the second predicts the oscillating frequencies for each case. The convergence classifier (CCANN) and frequency guess predictor (FGPANN) are seamlessly integrated into the simulation-based sizing loop, accelerating the overall optimization process. Secondly, a PVT regressor that inputs the circuit’s sizing and the nominal performances to estimate the PVT corner performances via multiple parallel artificial neural networks is proposed. Two control phases prevent the optimization process from being misled by inaccurate performance estimates. As such, this book details the optimal description of the input/output data relation that should be fulfilled. The developed description is mainly reflected in two of the system’s characteristics, the shape of the input data and its incorporation in the sizing optimization loop. An optimal description of these components should be such that the model should produce output data that fulfills the desired relation for the given training data once fully trained. Additionally, the model should be capable of efficiently generalizing the acquired knowledge in newer examples, i.e., never-seen input circuit topologies.

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Page Count

115

Publisher

Springer Nature

Published Date

ISBN 10

3031250990

ISBN 13

9783031250996

Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms

By: Frederico A.E. Rocha,Ricardo M.F. Martins,Nuno C.C. Lourenço,Nuno C.G. Horta

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Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms

By: Frederico A.E. Rocha,Ricardo M.F. Martins,Nuno C.C. Lourenço,Nuno C.G. Horta

This book applies to the scientific area of electronic design automation (EDA) and addresses the automatic sizing of analog integrated circuits (ICs). Particularly, this book presents an approach to enhance a state-of-the-art layout-aware circuit-level optimizer (GENOM-POF), by embedding statistical knowledge from an automatically generated gradient model into the multi-objective multi-constraint optimization kernel based on the NSGA-II algorithm. The results showed allow the designer to explore the different trade-offs of the solution space, both through the achieved device sizes, or the respective layout solutions.

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