Vlsi For Artificial Intelligence
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| Author | : Nikolas G Bourbakis |
| Publisher | : World Scientific |
| Total Pages | : 545 |
| Release | : 1998-05-05 |
| Genre | : Computers |
| ISBN | : 981449903X |
Contents:A New Way to Acquire Knowledge (H-Y Wang)An SPN Knowledge Representation Scheme (J Gattiker & N Bourbakis)On the Deep Structures of Word Problems and Their Construction (F Gomez)Resolving Conflicts in Inheritance Reasoning with Statistical Approach (C W Lee)Integrating High and Low Level Computer Vision for Scene Understanding (R Malik & S So)The Evolution of Commercial AI Tools: The First Decade (F Hayes-Roth)Reengineering: The AI Generation — Billions on the Table (J S Minor Jr)An Intelligent Tool for Discovering Data Dependencies in Relational DBS (P Gavaskar & F Golshani)A Case-Based Reasoning (CBR) Tool to Assist Traffic Flow (B Das & S Bayles)A Study of Financial Expert System Based on Flops (T Kaneko & K Takenaka)An Associative Data Parallel Compilation Model for Tight Integration of High Performance Knowledge Retrieval and Computation (A K Bansal)Software Automation: From Silly to Intelligent (J-F Xu et al.)Software Engineering Using Artificial Intelligence: The Knowledge Based Software Assistant (D White)Knowledge Based Derivation of Programs from Specifications (T Weight et al.)Automatic Functional Model Generation for Parallel Fault Design Error Simulations (S-E Chang & S A Szygenda)Visual Reverse Engineering Using SPNs for Automated Diagnosis and Functional Simulation of Digital Circuits (J Gattiker & S Mertoguno)The Impact of AI in VLSI Design Automation (M Mortazavi & N Bourbakis)The Automated Acquisition of Subcategorizations of Verbs, Nouns and Adjectives from Sample Sentences (F Gomez)General Method for Planning and Rendezvous Problems (K I Trovato)Learning to Improve Path Planning Performance (P C Chen)Incremental Adaptation as a Method to Improve Reactive Behavior (A J Hendriks & D M Lyons)An SPN-Neural Planning Methodology for Coordination of Multiple Robotic Arms with Constrained Placement (N Bourbakis & A Tascillo) Readership: Computer scientists, artificial intelligence practitioners and robotics users. keywords:
| Author | : Jose G. Delgado-Frias |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 411 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461537525 |
This book is an edited selection of the papers presented at the International Workshop on VLSI for Artifidal Intelligence and Neural Networks which was held at the University of Oxford in September 1990. Our thanks go to all the contributors and especially to the programme committee for all their hard work. Thanks are also due to the ACM-SIGARCH, the IEEE Computer Society, and the lEE for publicizing the event and to the University of Oxford and SUNY-Binghamton for their active support. We are particularly grateful to Anna Morris, Maureen Doherty and Laura Duffy for coping with the administrative problems. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE Artificial intelligence and neural network algorithms/computing have increased in complexity as well as in the number of applications. This in tum has posed a tremendous need for a larger computational power than can be provided by conventional scalar processors which are oriented towards numeric and data manipulations. Due to the artificial intelligence requirements (symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation) and neural network computing approach (non-programming and learning), a different set of constraints and demands are imposed on the computer architectures for these applications.
| Author | : Jose G. Delgado-Frias |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 318 |
| Release | : 2013-06-29 |
| Genre | : Computers |
| ISBN | : 1489913319 |
Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.
| Author | : Thaddeus J. Kowalski |
| Publisher | : Springer International Series in Engineering and Computer Science |
| Total Pages | : 248 |
| Release | : 1985-05-31 |
| Genre | : Computers |
| ISBN | : |
| Author | : Jose G. Delgado-Frias |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 285 |
| Release | : 2012-12-06 |
| Genre | : Technology & Engineering |
| ISBN | : 1461316197 |
| Author | : Sandeep Saini |
| Publisher | : CRC Press |
| Total Pages | : 329 |
| Release | : 2021-12-30 |
| Genre | : Technology & Engineering |
| ISBN | : 1000523810 |
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.
| Author | : Ibrahim (Abe) M. Elfadel |
| Publisher | : Springer |
| Total Pages | : 694 |
| Release | : 2019-03-15 |
| Genre | : Technology & Engineering |
| ISBN | : 3030046664 |
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
| Author | : Sandeep Saini |
| Publisher | : CRC Press |
| Total Pages | : 292 |
| Release | : 2021-12-31 |
| Genre | : Technology & Engineering |
| ISBN | : 1000523845 |
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.
| Author | : N Ranganathan |
| Publisher | : World Scientific |
| Total Pages | : 298 |
| Release | : 1995-06-30 |
| Genre | : Computers |
| ISBN | : 9814500232 |
This book covers parallel algorithms and architectures and VLSI chips for a range of problems in image processing, computer vision, pattern recognition and artificial intelligence. The specific problems addressed include vision and image processing tasks, Fast Fourier Transforms, Hough Transforms, Discrete Cosine Transforms, image compression, polygon matching, template matching, pattern matching, fuzzy expert systems and image rotation. The collection of papers gives the reader a good introduction to the state-of-the-art, while for an expert this serves as a good reference and a source of some new contributions in this field.
| Author | : Wei Zeng |
| Publisher | : |
| Total Pages | : 95 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
With the advance of Very Large Scale Integration (VLSI) technology, the design process of VLSI circuits becomes more complex, challenging, and time-consuming. Recent years have seen a rising trend of machine learning (ML) incorporated in VLSI design flow for better and more efficient design and implementation of integrated circuits. Explainable Artificial Intelligence (XAI) is an emerging technique that aims to perform prediction tasks while providing explanations for the predictions. XAI adds transparency and trustworthiness to ML models, leading to better human understanding and exploitation of the models. With ML being applied in VLSI design, it is desirable to adopt ideas from XAI for even better and more trustworthy outcomes of VLSI design. This dissertation explores the usage of Shapley Additive Explanation (SHAP)--a recent development in XAI, on different aspects and stages of VLSI design flow. Specifically, we propose three techniques that adopt SHAP in front-end and back-end design flows, including (a) SHAP-guided layout obfuscation for enhanced hardware security in split manufacturing, (b) explainable routability prediction, which accelerates the physical design flow and provides hints for improving the design, and (c) explainable-ML-guided approximate logic synthesis for area-efficient computing in error-tolerant applications. These are the first works that incorporate XAI into VLSI design methodology. All of them achieve better results than their conventional counterparts or existing works in similar settings.
