Neurochip Architectural Design For Pattern Recognition And
Classification
Author(s)
A.N. Kastania and M.P. Bekakos
Abstract
Neurochip architectural design for pattern
recognition and classification
A.N. Kastania & M.P. Bekakos
Department of Informatics, Athens University of Economics & Business,
Greece
Abstract
In this work the construction of a neural network to perform the task of
classification from a set of data for which the true classes are known is
investigated. Depending upon the knowledge strored in the architecture of an
Adaptive Logic Network (ALN) a specialized neurochip is built. The
performance of this architecture is evaluated using a challenging medical data
set and conclusions are drawn for the expandability of this neurochip for more
general cases.
1 Introduction
In the attempt to solve the classification problem different types of hybrid
neurochips integrating hardware and software components have been proposed.
In the MiND[l] system the neuroboard accelerates feedforward networks, radial
basis function networks and Kohonen feature maps. Reconfigurable Systolic
Array Neur
Keywords
Related Book
Other papers in this volume
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