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Titlebook: VLSI for Artificial Intelligence; José G. Delgado-Frias,Will R. Moore Book 1989 Kluwer Academic Publishers 1989 CMOS.Hardware.VLSI.algorit

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书目名称VLSI for Artificial Intelligence
编辑José G. Delgado-Frias,Will R. Moore
视频video
丛书名称The Springer International Series in Engineering and Computer Science
图书封面Titlebook: VLSI for Artificial Intelligence; José G. Delgado-Frias,Will R. Moore Book 1989 Kluwer Academic Publishers 1989 CMOS.Hardware.VLSI.algorit
出版日期Book 1989
关键词CMOS; Hardware; VLSI; algorithms; artificial intelligence; intelligence; knowledge base; learning; logic; pro
版次1
doihttps://doi.org/10.1007/978-1-4613-1619-0
isbn_softcover978-1-4612-8895-4
isbn_ebook978-1-4613-1619-0Series ISSN 0893-3405
issn_series 0893-3405
copyrightKluwer Academic Publishers 1989
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A 32 Bit Processor for Compiled Prologted machines to improve the execution speed. Among the logic programming languages, Prolog is actually the most widely used. For this reason the realization of efficient Prolog machines represents an essential background for the development of AI techniques.
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Carmel-1: A VLSI Architecture for Flat Concurrent Prologal” tool for programming highly parallel message-based computers. FCP is the language of our multicomputer CARMEL (Computer ARchitecture for Multiprocessing Execution of Logic programs). In this paper we describe CARMEL-1, a high performance uniprocessor component of that parallel architecture.
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A Self-Timed Circuit for a Prolog Machinell suited for implementing relational non-deterministic languages such as Prolog (Bekkers . 1984, 1986). A bi-processor Prolog-machine has been designed on the basis of this abstract memory: the main processor carries out system calls and performs the Prolog interpretation while a dedicated processo
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The Pattern Addressable Memory: Hardware for Associative Processinges for applicable entries via some form of pattern match. Such operations are so fundamental that they have become the basic execution mechanism in production systems and logic programming. However performing the actual pattern match over a rule database is computationally intensive. To offset this
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