Machine Learning Techniques For Vlsi Chip Design
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Machine Learning Techniques for VLSI Chip Design
MACHINE LEARNING TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arise when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, and implementation of the computational architecture for ML-DL. The future aspect of the ML-DL algorithm is to be available in the format of an integrated circuit (IC). A user can upgrade to the new algorithm by replacing an IC. This new book mainly deals with the adaption of computation blocks like hardware accelerators and novel nano-material for them based upon their application and to create a smart solution. This exciting new volume is an invaluable reference for beginners as well as engineers, scientists, researchers, and other professionals working in the area of VLSI architecture development.
Machine Learning in VLSI Computer-Aided Design
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
FeFET Devices, Trends, Technology and Applications
FeFET Devices, Trends, Technology and Applications is essential for anyone seeking an in-depth understanding of the latest advancements in ferroelectric devices, as it offers comprehensive insights into research techniques, novel materials, and the historical context of semiconductor development. This book serves as an encyclopedia of knowledge for state-of-the-art research techniques for the miniaturization of ferroelectric devices. This volume explores characteristics, novel materials used, modifications in device structure, and advancements in model FET devices. Though many devices following Moore’s Law and More-Moore are proposed, a complete history of existing and proposed semiconductor devices is now available here. This resource focuses on developments and research in emerging ferroelectric FET devices and their applications, providing unique coverage of topics covering recent advancements and novel concepts in the field of miniaturized ferroelectric devices.