Editor(s): Kung, H. T.; Sproull, R.; Steele, Guy L. Num Pages: 415 pages, biography. BIC Classification: TJF. Category: (P) Professional & Vocational. Dimension: 244 x 170 x 22. Weight in Grams: 740. . 2012. Softcover reprint of the original 1st ed. 1981. Paperback. . . . .

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Bibliographic Details

Title: VLSI Systems and Computations
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Publication Date: 2012
Language: English
ISBN 10: 3642684041
ISBN 13: 9783642684043
Binding: Soft cover
Condition: New

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Synopsis

The papers in this book were presented at the CMU Conference on VLSI Systems and Computations, held October 19-21, 1981 in Pittsburgh, Pennsylvania. The conference was organized by the Computer Science Department, Carnegie-Mellon University and was partially supported by the National Science Foundation and the Office of Naval Research. These proceedings focus on the theory and design of computational systems using VLSI. Until very recently, integrated-circuit research and development were concentrated in the device physics and fabrication design disciplines and in the integrated-circuit industry itself. Within the last few years, a community of researchers is growing to address issues closer to computer science: the relationship between computing structures and the physical structures that implement them; the specification and verification of computational procosses implemented in VLSI; the use of massively parallel computing made possible by VLSI; the design of special� purpose computing architectures; and the changes in general-purpose computer architecture that VLSI makes possible. It is likely that the future exploitation of VLSI technology depends as much on structural and design innovations as on advances in fabrication technology. The book is divided into nine sections: - Invited Papers. Six distinguished researchers from industry and academia presented invited papers. - Models of Computation. The papers in this section deal with abstracting the properties of VLSI circuits into models that can be used to analyze the chip area, time or energy required for a particular computation.

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