3 research outputs found
Object-Oriented Neural Simulation Tools For A Hypercube Parallel Machine
A substantial amount of work has recently been completed at the University of Central Florida in the development of an Artificial Neural Network (ANN) simulation environment that overcomes traditional implementation problems normally associated with these types of programs. Researchers addressing the development and application of ANN systems seek modifiability, expansibility and platform independence. Our system allows for these elements as well as parallel execution when a parallel hardware is available. This is accomplished by use of object-oriented programming and a Computer Aided Software Engineering (CASE) approach to the development environment that allows the user to modify the software describing the ANN model without understanding of the overall implementation details. A sophisticated Graphical User Interface (GUI) is provided to allow rapid construction and evaluation of complex large-scale neural models
Object-Oriented Neural Simulation Tools For A Hypercube Parallel Machine
A substantial amount of work has recently been completed at the University of Central Florida in the development of an Artificial Neural Network (ANN) simulation environment that overcomes traditional implementation problems normally associated with these types of programs. Researchers addressing the development and application of ANN systems seek modifiability, expansibility and platform independence. Our system allows for these elements as well as parallel execution when a parallel hardware is available. This is accomplished by use of object-oriented programming and a Computer Aided Software Engineering (CASE) approach to the development environment that allows the user to modify the software describing the ANN model without understanding of the overall implementation details. A sophisticated Graphical User Interface (GUI) is provided to allow rapid construction and evaluation of complex large-scale neural models. © 1992
The design of a neural network compiler
Computer simulation is a flexible and economical way for
rapid prototyping and concept evaluation with Neural
Network (NN) models. Increasing research on NNs has led
to the development of several simulation programs. Not
all simulations have the same scope. Some simulations
allow only a fixed network model and some are more
general. Designing a simulation program for general
purpose NN models has become a current trend nowadays
because of its flexibility and efficiency. A proper
programming language specifically for NN models is
preferred since the existing high-level languages such as
C are for NN designers from a strong computer background.
The program translations for NN languages come from
combinations which are either interpreter and/or
compiler. There are also various styles of programming
languages such as a procedural, functional, descriptive
and object-oriented.
The main focus of this thesis is to study the
feasibility of using a compiler method for the
development of a general-purpose simulator - NEUCOMP that
compiles the program written as a list of mathematical
specifications of the particular NN model and translates
it into a chosen target program. The language supported
by NEUCOMP is based on a procedural style. Information
regarding the list of mathematical statements required by
the NN models are written in the program. The
mathematical statements used are represented by scalar,
vector and matrix assignments. NEUCOMP translates these
expressions into actual program loops.
NEUCOMP enables compilation of a simulation program
written in the NEUCOMP language for any NN model,
contains graphical facilities such as portraying the NN
architecture and displaying a graph of the result during
training and finally to have a program that can run on a
parallel shared memory multi-processor system