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Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
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VLSI - Compatible Implementations for Artificial Neural Networks (Hardcover, 1997 ed.)
Loot Price: R2,915
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VLSI - Compatible Implementations for Artificial Neural Networks (Hardcover, 1997 ed.)
Series: The Springer International Series in Engineering and Computer Science, 382
Expected to ship within 10 - 15 working days
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VLSI-Compatible Implementations for Artificial Neural Networks
introduces the basic premise of the authors' approach to
biologically-inspired and VLSI-compatible definition, simulation,
and implementation of artificial neural networks. In addition, the
book develops a set of guidelines for general hardware
implementation of ANNs. These guidelines are then used to find
solutions for the usual difficulties encountered in any potential
work, and as guidelines by which to reach the best compromise when
several options exist. Furthermore, system-level consequences of
using the proposed techniques in future submicron technologies with
almost-linear MOS devices are discussed. While the major emphasis
in this book is to develop neural networks optimized for
compatibility with their implementation media, the work has also
been extended to the design and implementation of a fully-quadratic
ANN based on the desire to have network definitions epitomized for
both efficient discrimination of closed-boundary circular areas and
ease of implementation in a CMOS technology. VLSI-Compatible
Implementations for Artificial Neural Networks implements a
comprehensive approach which starts with an analytical evaluation
of specific artificial networks. This provides a clear geometrical
interpretation of the behavior of different variants of these
networks. In combination with the guidelines developed towards a
better final implementation, these concepts have allowed the
authors to conquer various problems encountered and to make
effective compromises. Then, to facilitate the investigation of the
models needed when more difficult problems must be faced, a custom
simulating program for various cases is developed.Finally, in order
to demonstrate the authors' findings and expectations, several VLSI
integrated circuits have been designed, fabricated, and tested.
VLSI-Compatible Implementations for Artificial Neural Networks
serves as an excellent reference source and may be used as a text
for advanced courses on the subject.
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