Computers For Artificial Intelligence Processing
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Computers for Artificial Intelligence Processing
Author: Benjamin W. Wah
language: en
Publisher: Wiley-Interscience
Release Date: 1990-10-12
The present book supports the increasing complexity and the growing need for computational power of artificial intelligence (AI) by providing comprehensive treatments of new hardware and software engineering met in AI language design and applications. The book is a collection of 16 substantial papers (chapters), the contributors being 51 well-known researchers in the AI fields. The papers are grouped into the following five sections: Section 1 represents a well documented survey on symbolic processing computers. Section 2 (Language-based AI Architectures) supports the design and implementation of AI language-oriented computers. Three (2-4) chapters are devoted to (computer architecture concerning) sequential Lisp processing: architectural features of Lisp computers, Symbolics’ Lisp computer architecture, memory management and performance evaluation of a Lisp machine system. Other three (5-7) chapters discuss multiprocessing and parallel processing of Lisp (and general functional) programs. The last two chapters of section 2 are presenting architectures supporting object-oriented programming (Smalltalk) and production systems. Section 3 (Multiprocessor AI Architecture) contains two (10-11) chapters, dealing with Connection Machine architecture and its applications, and with the design of data/knowledge base machines for AI processing. Section 4 (Connectionist Architectures and Applications) include two (12-13) chapters, illustrating the connectionist model architecture design and learning. Section 5 (Software Architectures for AI Applications) is made up of three (14-16) chapters, analysing the relationship between AI and software engineering, the development tools for AI programs, and the problem of AI hardware and software reliability. This book addresses a wide range of AI readers, from beginners to professionals. It carries forth doubtless qualities: compact and well-dimensioned chapters, comprehensively written by AI remarkable professionals, covering up-to-date AI topics and trends.
Hardware Technologies for Artificial Intelligence
In this comprehensive reference work for researchers, engineers, and students, Kawahara provides a one-stop exploration of next-generation computing at the LSI circuit level, with a focus on the integration of AI, advanced LSI design, Ising machines, and memory innovations. While current GPUs have high parallel processing capabilities suitable for computations on large datasets, their power consumption is approaching its limit and requires further development. Additionally, edge computing is becoming increasingly important alongside cloud computing. Amid these significant technological trends, this book provides readers with important insights into next-generation computing, namely (1) neural network (artificial intelligence) LSIs and their low power and high performance, (2) hardware design technology for combinatorial optimization problems and Ising machines, and (3) semiconductor memory and data-centric computing. Kawahara first describes the basics of LSI design and neural networks before then describing their large-scale integration, power efficiency and performance enhancements. He then also explains hardware design techniques for Ising machines, offers case studies of fully coupled Ising machine LSI. Last, he discusses the basics of semiconductor memory, near/in-memory AI computing, and then examines the future prospects. Readers will be able to apply this knowledge to the design and manufacture of such devices to overcome the limitations of current hardware and computational methods, driving future advancements in artificial intelligence and optimization. This is a valuable reference for students, engineers and researchers alike in this field. As it begins with the basics, it enables all readers to follow the direction of next-generation computing and its important technical content without the need for prior knowledge or reference to other books.
Parallel Computation and Computers for Artificial Intelligence
It has been widely recognized that artificial intelligence computations offer large potential for distributed and parallel processing. Unfortunately, not much is known about designing parallel AI algorithms and efficient, easy-to-use parallel computer architectures for AI applications. The field of parallel computation and computers for AI is in its infancy, but some significant ideas have appeared and initial practical experience has become available. The purpose of this book has been to collect in one volume contributions from several leading researchers and pioneers of AI that represent a sample of these ideas and experiences. This sample does not include all schools of thought nor contributions from all leading researchers, but it covers a relatively wide variety of views and topics and in this sense can be helpful in assessing the state ofthe art. We hope that the book will serve, at least, as a pointer to more specialized literature and that it will stimulate interest in the area of parallel AI processing. It has been a great pleasure and a privilege to cooperate with all contributors to this volume. They have my warmest thanks and gratitude. Mrs. Birgitta Knapp has assisted me in the editorial task and demonstrated a great deal of skill and patience. Janusz S. Kowalik vii INTRODUCTION Artificial intelligence (AI) computer programs can be very time-consuming.