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This book presents a hands-on approach for solving electronic design automation problems with modern machine intelligence techniques by including step-by-step development of commercial grade design applications including resistance estimation, capacitance estimation, cell classification and others using dataset extracted from designs at 20nm. It walks the reader step by step in building solution flow for EDA problems with Python and Tensorflow.Intended audience includes design automation engineers, managers, executives, research professionals, graduate students, Machine learning enthusiasts, EDA and CAD developers, mentors, and the merely inquisitive. It is organized to serve as a compendium to a beginner, a ready reference to intermediate and source for an expert.
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:
Computers have been employed for some time in engineering design mainly as numerical or graphical tools to assist analysis and draughting. The advent of the technology of artificial intelligence and expert systems has enabled computers to be applied to less deterministic design tasks which require symbolic manipulation and reasoning, instead of only routine number processing. This book presents recent examples of such applications, focusing on mechanical and manufacturing design. The term 'design' is interpreted here in its wider sense to include creative activities such as planning. The book covers a wide spectrum of design operations ranging from component and product design through to process, tooling and systems design. Its aim is to expose researchers, engineers and engineering designers to several developments in the emerging field of intelligent CAD and to alert them of the possibilites and opportunities in this exciting field.
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.
Artificial Intelligence in Engineering Design is a three-volume edited collection of key papers from the field of AI and design, aimed at providing a state-of-the art description of the field, and focusing on how ideas and methods from artificial intelligence can help engineers in the design of physical artifacts and processes. The books survey a wide variety of applications in the areas of civil, chemical, electrical, computer, VLSI, and mechanical engineering.
Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI. This book examines: Differences between and limitations of automated, autonomous, and human decision-making Unique advantages of autonomous AI for real-time decision-making, with use cases How to design an autonomous AI from modular components and document your designs
The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
How should we train? What should we learn? What is our value? Disruptive technologies have increased speculation about what it means to be an architect. Innovations simultaneously offer great promise and potential risk to design practice. This volume identifies the game-changing trends driven by technology, and the opportunities they provide for architecture, urbanism and design. It advocates for an approach of intelligent control that transforms practice with specialist knowledge of technological models and systems. It features new developments in automation, generative design, augmented reality, videogame urbanism, artificial intelligence and robotics, as well as lived experiences within a continually shifting landscape. Showcasing evolving research, it discusses the cultural, social, environmental and political implications of various technological trajectories. In doing so it speculates upon future urban, spatial, aesthetic and formal possibilities within architecture. The future is already here. Now is the time to act. Features: Austrian Institute of Technology AiT - City Intelligence Lab CiT, Bryden Wood, Mollie Claypool, Soomeen Hahm, Hawkins\Brown, LASSA Architects, The Living, Danil Nagy, Odico Construction Robotics, Stefana Parascho, Luke Caspar Pearson, SHoP Architects, Kostas Terzidis, Mette Ramsgaard Thomsen and Sandra Youkhana.
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.