Neural networks and systolic array design
Author(s)
Bibliographic Information
Neural networks and systolic array design
(Series in machine perception and artificial intelligence / editors, H. Bunke, P.S.P. Wang, v. 49)
World Scientific, c1992
Available at 3 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.
Table of Contents
- Neural Networks and Systolic Arrays: Models and Integration (D Zhang & S K Pal)
- Systolic Array Methodology for a Neural Model to Solve the Mixture Problem (R M Perez et al.)
- Morphological Endmember Identification and Its Systolic Array Design (P L Aguilar et al.)
- MANTRA I: A Systolic Array for Neural Computation (M A Viredaz & P Ienne)
- Mixed-Signal Neuro-Fuzzy Processor Implementations: Sequential Architectures and Circuit-Level Description (J Madrenas & E Alarcon)
- CMAC Neural Networks and Systolic Implementation (B D Liu et al.)
- Quadrant Interlocking Factorization on Systolic and Wavefront Array Processors (M P Bekakos et al.)
- Systolic S.O.M. Neural Network for Hyperspectral Image Classification (P Martinez et al.)
- Optimizing and Learning Algorithm for Feedforward Neural Networks and Its Implementation by Systolic Array (P B Burgos)
- Parallel ANN Architecture for Fuzzy Patterns (D Zhang & S K Pal)
- Pipelined Systolic Arrays for Time-Delay Neural Networks (D Zhang & S K Pal)
- An Integrated Intelligent Classification Engine (I2CE) for Biosignal Engineering (A N Kastania & M P Bekakos)
- Multiplierless Designs for Artificial Neural Networks (H K Kwan)
- A VLSI System for Intelligent Decision Making a Real-Time (N Ranganathan & M I Patel)
- Reconfigurable Hardware Systolic Array for Real-Time Compartmental Modeling of Large-Scale Artificial Nervous Systems (M Korkin)
- Implementing and Mapping ANNs on Reconfigurable Mesh Massively Parallel Architectures (W N Li & J J Jenq).
by "Nielsen BookData"