Machine Learning in VLSI Computer-Aided Design

Machine Learning in VLSI Computer-Aided Design PDF Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 9783030046651
Category : Technology & Engineering
Languages : en
Pages : 694

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Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Machine Learning in VLSI Computer-Aided Design

Machine Learning in VLSI Computer-Aided Design PDF Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 3030046664
Category : Technology & Engineering
Languages : en
Pages : 694

Get Book

Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Mobile Computing and Sustainable Informatics

Mobile Computing and Sustainable Informatics PDF Author: Subarna Shakya
Publisher: Springer Nature
ISBN: 9811618666
Category : Technology & Engineering
Languages : en
Pages : 875

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Book Description
This book gathers selected high-quality research papers presented at International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2021) organized by Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal, during 29–30 January 2021. The book discusses recent developments in mobile communication technologies ranging from mobile edge computing devices, to personalized, embedded and sustainable applications. The book covers vital topics like mobile networks, computing models, algorithms, sustainable models and advanced informatics that supports the symbiosis of mobile computing and sustainable informatics.

VLSI-SoC: Design Trends

VLSI-SoC: Design Trends PDF Author: Andrea Calimera
Publisher: Springer Nature
ISBN: 3030816419
Category : Computers
Languages : en
Pages : 364

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Book Description
This book contains extended and revised versions of the best papers presented at the 28th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2020, held in Salt Lake City, UT, USA, in October 2020.* The 16 full papers included in this volume were carefully reviewed and selected from the 38 papers (out of 74 submissions) presented at the conference. The papers discuss the latest academic and industrial results and developments as well as future trends in the field of System-on-Chip (SoC) design, considering the challenges of nano-scale, state-of-the-art and emerging manufacturing technologies. In particular they address cutting-edge research fields like low-power design of RF, analog and mixed-signal circuits, EDA tools for the synthesis and verification of heterogenous SoCs, accelerators for cryptography and deep learning and on-chip Interconnection system, reliability and testing, and integration of 3D-ICs. *The conference was held virtually.

VLSI and Hardware Implementations using Modern Machine Learning Methods

VLSI and Hardware Implementations using Modern Machine Learning Methods PDF Author: Sandeep Saini
Publisher: CRC Press
ISBN: 1000523845
Category : Technology & Engineering
Languages : en
Pages : 328

Get Book

Book Description
Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.

The IoT Physical Layer

The IoT Physical Layer PDF Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 3319931008
Category : Technology & Engineering
Languages : en
Pages : 382

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Book Description
This book documents some of the most recent advances on the physical layer of the Internet of Things (IoT), including sensors, circuits, and systems. The application area selected for illustrating these advances is that of autonomous, wearable systems for real-time medical diagnosis. The book is unique in that it adopts a holistic view of such systems and includes not only the sensor and processing subsystems, but also the power, communication, and security subsystems. Particular attention is paid to the integration of these IoT subsystems as well as the prototyping platforms needed for achieving such integration. Other unique features include the discussion of energy-harvesting subsystems to achieve full energy autonomy and the consideration of hardware security as a requirement for the integrity of the IoT physical layer. One unifying thread of the various designs considered in this book is that they have all been fabricated and tested in an advanced, low-power CMOS process, namely GLOBALFOUNDRIES 65nm CMOS LPe.

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation PDF Author: João P. S. Rosa
Publisher: Springer Nature
ISBN: 3030357430
Category : Technology & Engineering
Languages : en
Pages : 101

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Book Description
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.

Machine Learning

Machine Learning PDF Author: Hamed Farhadi
Publisher: BoD – Books on Demand
ISBN: 1789237521
Category : Computers
Languages : en
Pages : 230

Get Book

Book Description
The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

Performance-Driven Surrogate Modeling of High-Frequency Structures

Performance-Driven Surrogate Modeling of High-Frequency Structures PDF Author: Slawomir Koziel
Publisher: Springer Nature
ISBN: 303038926X
Category : Technology & Engineering
Languages : en
Pages : 403

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Book Description
This book discusses surrogate modeling of high-frequency structures including antenna and microwave components. The focus is on constrained or performance-driven surrogates. The presented techniques aim at addressing the limitations of conventional modeling methods, pertinent to the issues of dimensionality and parameter ranges that need to be covered by the surrogate to ensure its design utility. Within performance-driven methodologies, mitigation of these problems is achieved through appropriate confinement of the model domain, focused on the regions promising from the point of view of the relevant design objectives. This enables the construction of reliable surrogates at a fraction of cost required by conventional methods, and to accomplish the modeling tasks where other techniques routinely fail. The book provides a broad selection of specific frameworks, extensively illustrated using examples of real-world microwave and antenna structures along with numerous design examples. Furthermore, the book contains introductory material on data-driven and physics-based surrogates. The book will be useful for the readers working in the area of high-frequency electronics, including microwave engineering, antenna design, microwave photonics, magnetism, especially those that utilize electromagnetic (EM) simulation models in their daily routines. Covers performance-driven and constrained modeling methods, not available in other books to date; Discusses of a wide range of practical case studies including a variety of microwave and antenna structures; Includes design applications of the presented modeling frameworks, including single- and multi-objective parametric optimization.

Inventive Systems and Control

Inventive Systems and Control PDF Author: V. Suma
Publisher: Springer Nature
ISBN: 9811613958
Category : Technology & Engineering
Languages : en
Pages : 995

Get Book

Book Description
This book presents selected papers from the 5th International Conference on Inventive Systems and Control (ICISC 2021), held on 7–8 January 2021 at JCT College of Engineering and Technology, Coimbatore, India. The book includes an analysis of the class of intelligent systems and control techniques that utilises various artificial intelligence technologies, where there are no mathematical models and systems available to make them remain controlled. Inspired by various existing intelligent techniques, the primary goal is to present the emerging innovative models to tackle the challenges faced by the existing computing and communication technologies. The proceedings of ICISC 2021 aim at presenting the state-of-the-art research developments, trends, and solutions for the challenges faced by the intelligent systems and control community with the real-world applications. The included research articles feature the novel and unpublished research works on intelligent system representation and control.

Machine Learning in VLSI Computer-Aided Design

Machine Learning in VLSI Computer-Aided Design PDF Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 9783030046651
Category : Technology & Engineering
Languages : en
Pages : 694

View

Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Machine Learning in VLSI Computer-Aided Design

Machine Learning in VLSI Computer-Aided Design PDF Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 3030046664
Category : Technology & Engineering
Languages : en
Pages : 694

View

Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Mobile Computing and Sustainable Informatics

Mobile Computing and Sustainable Informatics PDF Author: Subarna Shakya
Publisher: Springer Nature
ISBN: 9811618666
Category : Technology & Engineering
Languages : en
Pages : 875

View

Book Description
This book gathers selected high-quality research papers presented at International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2021) organized by Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal, during 29–30 January 2021. The book discusses recent developments in mobile communication technologies ranging from mobile edge computing devices, to personalized, embedded and sustainable applications. The book covers vital topics like mobile networks, computing models, algorithms, sustainable models and advanced informatics that supports the symbiosis of mobile computing and sustainable informatics.

VLSI-SoC: Design Trends

VLSI-SoC: Design Trends PDF Author: Andrea Calimera
Publisher: Springer Nature
ISBN: 3030816419
Category : Computers
Languages : en
Pages : 364

View

Book Description
This book contains extended and revised versions of the best papers presented at the 28th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2020, held in Salt Lake City, UT, USA, in October 2020.* The 16 full papers included in this volume were carefully reviewed and selected from the 38 papers (out of 74 submissions) presented at the conference. The papers discuss the latest academic and industrial results and developments as well as future trends in the field of System-on-Chip (SoC) design, considering the challenges of nano-scale, state-of-the-art and emerging manufacturing technologies. In particular they address cutting-edge research fields like low-power design of RF, analog and mixed-signal circuits, EDA tools for the synthesis and verification of heterogenous SoCs, accelerators for cryptography and deep learning and on-chip Interconnection system, reliability and testing, and integration of 3D-ICs. *The conference was held virtually.

VLSI and Hardware Implementations using Modern Machine Learning Methods

VLSI and Hardware Implementations using Modern Machine Learning Methods PDF Author: Sandeep Saini
Publisher: CRC Press
ISBN: 1000523845
Category : Technology & Engineering
Languages : en
Pages : 328

View

Book Description
Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.

The IoT Physical Layer

The IoT Physical Layer PDF Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 3319931008
Category : Technology & Engineering
Languages : en
Pages : 382

View

Book Description
This book documents some of the most recent advances on the physical layer of the Internet of Things (IoT), including sensors, circuits, and systems. The application area selected for illustrating these advances is that of autonomous, wearable systems for real-time medical diagnosis. The book is unique in that it adopts a holistic view of such systems and includes not only the sensor and processing subsystems, but also the power, communication, and security subsystems. Particular attention is paid to the integration of these IoT subsystems as well as the prototyping platforms needed for achieving such integration. Other unique features include the discussion of energy-harvesting subsystems to achieve full energy autonomy and the consideration of hardware security as a requirement for the integrity of the IoT physical layer. One unifying thread of the various designs considered in this book is that they have all been fabricated and tested in an advanced, low-power CMOS process, namely GLOBALFOUNDRIES 65nm CMOS LPe.

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation PDF Author: João P. S. Rosa
Publisher: Springer Nature
ISBN: 3030357430
Category : Technology & Engineering
Languages : en
Pages : 101

View

Book Description
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.

Machine Learning

Machine Learning PDF Author: Hamed Farhadi
Publisher: BoD – Books on Demand
ISBN: 1789237521
Category : Computers
Languages : en
Pages : 230

View

Book Description
The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

Performance-Driven Surrogate Modeling of High-Frequency Structures

Performance-Driven Surrogate Modeling of High-Frequency Structures PDF Author: Slawomir Koziel
Publisher: Springer Nature
ISBN: 303038926X
Category : Technology & Engineering
Languages : en
Pages : 403

View

Book Description
This book discusses surrogate modeling of high-frequency structures including antenna and microwave components. The focus is on constrained or performance-driven surrogates. The presented techniques aim at addressing the limitations of conventional modeling methods, pertinent to the issues of dimensionality and parameter ranges that need to be covered by the surrogate to ensure its design utility. Within performance-driven methodologies, mitigation of these problems is achieved through appropriate confinement of the model domain, focused on the regions promising from the point of view of the relevant design objectives. This enables the construction of reliable surrogates at a fraction of cost required by conventional methods, and to accomplish the modeling tasks where other techniques routinely fail. The book provides a broad selection of specific frameworks, extensively illustrated using examples of real-world microwave and antenna structures along with numerous design examples. Furthermore, the book contains introductory material on data-driven and physics-based surrogates. The book will be useful for the readers working in the area of high-frequency electronics, including microwave engineering, antenna design, microwave photonics, magnetism, especially those that utilize electromagnetic (EM) simulation models in their daily routines. Covers performance-driven and constrained modeling methods, not available in other books to date; Discusses of a wide range of practical case studies including a variety of microwave and antenna structures; Includes design applications of the presented modeling frameworks, including single- and multi-objective parametric optimization.

Inventive Systems and Control

Inventive Systems and Control PDF Author: V. Suma
Publisher: Springer Nature
ISBN: 9811613958
Category : Technology & Engineering
Languages : en
Pages : 995

View

Book Description
This book presents selected papers from the 5th International Conference on Inventive Systems and Control (ICISC 2021), held on 7–8 January 2021 at JCT College of Engineering and Technology, Coimbatore, India. The book includes an analysis of the class of intelligent systems and control techniques that utilises various artificial intelligence technologies, where there are no mathematical models and systems available to make them remain controlled. Inspired by various existing intelligent techniques, the primary goal is to present the emerging innovative models to tackle the challenges faced by the existing computing and communication technologies. The proceedings of ICISC 2021 aim at presenting the state-of-the-art research developments, trends, and solutions for the challenges faced by the intelligent systems and control community with the real-world applications. The included research articles feature the novel and unpublished research works on intelligent system representation and control.