Machine Learning For Vlsi Computer Aided Design
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Machine Learning In Vlsi Computer Aided Design
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Author : Ibrahim (Abe) M. Elfadel
language : en
Publisher: Springer
Release Date : 2019-03-15
Machine Learning In Vlsi Computer Aided Design written by Ibrahim (Abe) M. Elfadel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Technology & Engineering categories.
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 thatI 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 For Vlsi Computer Aided Design
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Author : Mohamed Baker Alawieh
language : en
Publisher:
Release Date : 2020
Machine Learning For Vlsi Computer Aided Design written by Mohamed Baker Alawieh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Consumer electronics have become an integral part of people’s life putting at their disposal immense computational power that provides numerous applications. This has been enabled by the ceaseless down scaling of integrated circuit (IC) technologies which keeps pushing the performance boundary. Such scaling continues to drive, as a byproduct, an up scale in the challenges associated with circuit design and manufacturability. Among the major challenges facing modern IC Computer Aided Design (CAD) are those related to manufacturing and yield which are manifested through: (1) expensive modeling and simulation (e.g. large and complex designs); (2) entangled design and manufacturability (e.g., yield sensitive to design patterns); and (3) strict design constraints (e.g., high yield). While these challenges associated with retaining the robustness of modern designs continue to exacerbate, Very Large-Scale Integration (VLSI) CAD is becoming more critical, yet more challenging. Parallel to these developments are the recent advancements in Machine Learning (ML) which have altered the perception of computing. This dissertation attempts to address the aforementioned challenges in VLSI CAD through machine learning techniques. Our research includes efficient analog modeling, learning-assisted physical design and yield analysis, and model adaptation schemes tailored to the ever-changing IC environment. With aggressive scaling, process variation manifests itself among the most prominent factors limiting the yield of analog and mixed-signal (AMS) circuits. In modern ICs, the expensive simulation cost is one of the challenges facing accurate modeling of this variation. Our study develops a novel semi-supervised learning framework for AMS design modeling that is capable of significantly reducing the modeling cost. In addition, a new perspective towards incorporating sparsity in the modeling task is proposed. At the lithography stage, resolution enhancement techniques in general, and Sub Resolution Assist Feature (SRAF) insertion in particular, have become indispensable given the ever shrinking feature size. While different approaches have been proposed for SRAF insertion, the trade-off between efficiency and accuracy is still the governing principle. To address this, we recast the SRAF insertion process as an image translation task and propose a deep learning-based approach for efficient SRAF insertion. Besides, with complex designs, challenges at the physical design stage have exacerbated. Therefore, across-layers information sharing has become imperative for timely design closure. Particularly, in modern Field Programmable Gate Array (FPGA) place and route flows, leveraging routing congestion information during placement has demonstrated imperative benefit. Our study develops a new congestion prediction approach for large-scale FPGA designs that achieves superior prediction accuracy. Moreover, during fabrication, a critical first step towards improving production yield is to identify the underlying factors that contribute most to yield loss. And for that, wafer map defect analysis is a key. We present a novel wafer map defect pattern classification framework using confidence-aware deep selective learning. The use of ML for CAD tasks has the promise of delivering better performance and efficiency. However, one of the main characteristics of the field is that it is evolving with a fast rate of change. Therefore, by the time enough data is available to train accurate models under a given environment, changes start to occur. In this sense, the frequent restarts limit the returns on developing ML models. To address this, we develop a framework for the fast migration of classification models across different environments. Our approaches are validated with extensive experiments where they proved capable of advancing the VLSI CAD flow
Advancing Vlsi Through Machine Learning
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Author : Abhishek Narayan Tripathi
language : en
Publisher: CRC Press
Release Date : 2025-03-31
Advancing Vlsi Through Machine Learning written by Abhishek Narayan Tripathi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-31 with Technology & Engineering categories.
This book explores the synergy between very large-scale integration (VLSI) and machine learning (ML) and its applications across various domains. It investigates how ML techniques can enhance the design and testing of VLSI circuits, improve power efficiency, optimize layouts, and enable novel architectures. This book bridges the gap between VLSI and ML, showcasing the potential of this integration in creating innovative electronic systems, advancing computing capabilities, and paving the way for a new era of intelligent devices and technologies. Additionally, it covers how VLSI technologies can accelerate ML algorithms, enabling more efficient and powerful data processing and inference engines. It explores both hardware and software aspects, covering topics like hardware accelerators, custom hardware for specific ML tasks, and ML-driven optimization techniques for chip design and testing. This book will be helpful for academicians, researchers, postgraduate students, and those working in ML-driven VLSI.
Machine Learning Applications In Electronic Design Automation
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Author : Haoxing Ren
language : en
Publisher: Springer Nature
Release Date : 2023-01-01
Machine Learning Applications In Electronic Design Automation written by Haoxing Ren and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Technology & Engineering categories.
This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.
Opto Vlsi Devices And Circuits For Biomedical And Healthcare Applications
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Author : Ankur Kumar
language : en
Publisher: CRC Press
Release Date : 2023-09-04
Opto Vlsi Devices And Circuits For Biomedical And Healthcare Applications written by Ankur Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-04 with Technology & Engineering categories.
The text comprehensively discusses the latest Opto-VLSI devices and circuits useful for healthcare and biomedical applications. It further emphasizes the importance of smart technologies such as artificial intelligence, machine learning, and the internet of things for the biomedical and healthcare industries. Discusses advanced concepts in the field of electro-optics devices for medical applications. Presents optimization techniques including logical effort, particle swarm optimization and genetic algorithm to design Opto-VLSI devices and circuits. Showcases the concepts of artificial intelligence and machine learning for smart medical devices and data auto-collection for distance treatment. Covers advanced Opto-VLSI devices including a field-effect transistor and optical sensors, spintronic and photonic devices. Highlights application of flexible electronics in health monitoring and artificial intelligence integration for better medical devices. The text presents the advances in the fields of optics and VLSI and their applicability in diverse areas including biomedical engineering and the healthcare sector. It covers important topics such as FET biosensors, optical biosensors and advanced optical materials. It further showcases the significance of smart technologies such as artificial intelligence, machine learning and the internet of things for the biomedical and healthcare industries. It will serve as an ideal design book for senior undergraduate, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer engineering and biomedical engineering.
Mobile Computing And Sustainable Informatics
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Author : Subarna Shakya
language : en
Publisher: Springer Nature
Release Date : 2021-07-22
Mobile Computing And Sustainable Informatics written by Subarna Shakya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-22 with Technology & Engineering categories.
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.
Machine Learning In Modeling And Simulation
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Author : Timon Rabczuk
language : en
Publisher: Springer Nature
Release Date : 2023-10-03
Machine Learning In Modeling And Simulation written by Timon Rabczuk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-03 with Technology & Engineering categories.
Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time. With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering.
Proceedings Of The Third International Conference On Cognitive And Intelligent Computing Volume 2
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Author : Amit Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-02-25
Proceedings Of The Third International Conference On Cognitive And Intelligent Computing Volume 2 written by Amit Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-25 with Computers categories.
This book presents original, peer-reviewed select articles from the International Conference on Cognitive and Intelligent Computing (ICCIC-2023), held on December 8–9, 2023, at Hyderabad, in India. The book focuses on the comprehensive nature of computational intelligence, cognitive computing, AI, ML, and DL in order to highlight its role in the modelling, identification, optimisation, prediction, forecasting, and control of future intelligent systems. It includes contributions from a methodological/application standpoint in understanding artificial intelligence and machine learning approaches and their capabilities in solving a wide range of problems in the real world.
Ai Enabled Electronic Circuit And System Design
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Author : Ali Iranmanesh
language : en
Publisher: Springer Nature
Release Date : 2025-01-27
Ai Enabled Electronic Circuit And System Design written by Ali Iranmanesh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-27 with Computers categories.
As our world becomes increasingly digital, electronics underpin nearly every industry. Understanding how AI enhances this foundational technology can unlock innovations, from smarter homes to more powerful gadgets, offering vast opportunities for businesses and consumers alike. This book demystifies how AI streamlines the creation of electronic systems, making them smarter and more efficient. With AI’s transformative impact on various engineering fields, this resource provides an up-to-date exploration of these advancements, authored by experts actively engaged in this dynamic field. Stay ahead in the rapidly evolving landscape of AI in engineering with “AI-Enabled Electronic Circuit and System Design: From Ideation to Utilization,” your essential guide to the future of electronic systems. !--[endif]--A transformative guide describing how revolutionizes electronic design through AI integration. Highlighting trends, challenges and opportunities; Demystifies complex AI applications in electronic design for practical use; Leading insights, authored by top experts actively engaged in the field; Offers a current, relevant exploration of significant topics in AI’s role in electronic circuit and system design. Editor’s bios. Dr. Ali A. Iranmanesh is the founder and CEO of Silicon Valley Polytechnic Institute. He has received his Bachelor of Science in Electrical Engineering from Sharif University of Technology (SUT), Tehran, Iran, and both his master’s and Ph.D. degrees in Electrical Engineering and Physics from Stanford University in Stanford, CA. He additionally holds a master’s degree in business administration (MBA) from San Jose State University in San Jose, CA. Dr. Iranmanesh is the founder and chairman of the International Society for Quality Electronic Design (ISQED). Currently, he serves as the CEO of Innovotek. Dr. Iranmanesh has been instrumental in advancing semiconductor technologies, innovative design methodologies, and engineering education. He holds nearly 100 US and international patents, reflecting his signifi cant contributions to the field. Dr. Iranmanesh is the Senior life members of EEE, senior member of the American Society for Quality, co-founder and Chair Emeritus of the IEEE Education Society of Silicon Valley, Vice Chair Emeritus of the IEEE PV chapter, and recipient of IEEE Outstanding Educator Award. Dr. Hossein Sayadi is a Tenure-Track Assistant Professor and Associate Chair in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He earned his Ph.D. in Electrical and Computer Engineering from George Mason University in Fairfax, Virginia, and an M.Sc. in Computer Engineering from Sharif University of Technology in Tehran, Iran. As a recognized researcher with over 14 years of research experience, Dr. Sayadi is the founder and director of the Intelligent, Secure, and Energy-Efficient Computing (iSEC) Lab at CSULB. His research focuses on advancing hardware security and trust, AI and machine learning, cybersecurity, and energy-efficient computing, addressing critical challenges in modern computing and cyber-physical systems. He has authored over 75 peer-reviewed publications in leading conferences and journals. Dr. Sayadi is the CSU STEM-NET Faculty Fellow, with his research supported by multiple National Science Foundation (NSF) grants and awards from CSULB and the CSU Chancellor’s Office. He has contributed to various international conferences as an organizer and program committee member, including as the TPC Chair for the 2024 and 2025 IEEE ISQED.
Performance Driven Surrogate Modeling Of High Frequency Structures
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Author : Slawomir Koziel
language : en
Publisher: Springer Nature
Release Date : 2020-02-19
Performance Driven Surrogate Modeling Of High Frequency Structures written by Slawomir Koziel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-19 with Technology & Engineering categories.
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.