A brief summary of neural networks is presented which concentrates on the design constraints imposed. Major design issues are discussed together with analysis methods and the chosen solutions. Although the system will be capable of running on most transputer architectures, it currently is being implemented on a 40-transputer system connected to a toroidal architecture. Predictions show a performance level equivalent to that of a highly optimized simulator running on the SX-2 supercomputer
Obtaining optimal solutions for engineering design problems is often expensive because the process t...
Parallelism and distribution have been considered the key features of neural processing. The term pa...
Over the last few years tremendous progress has been made in neuroscience by employing simulation to...
Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applyi...
In this paper we describe the design, development, and performance of a neural network simulator for...
Contains fulltext : 18657.pdf (publisher's version ) (Open Access)In this thesis, ...
While conventional computers must be programmed in a logical fashion by a person who thoroughly unde...
Over the last few years tremendous progress has been made in neuroscience by employing simulation to...
The Back Propagation Model for neural network simulation is a very simple and very popular model for...
Artificial neural networks are systems composed of interconnected simple computing units known as a...
Neural networks have attracted much interest recently, and using parallel architectures to simulate ...
This paper describes the specification and implementation of PANNS (Parallel Artifical Neural Networ...
This thesis is about parallelizing the training phase of a feed-forward, artificial neural network....
Neural networks tend to fall into two general categories: (1) software simulations, or (2) custom ha...
This paper presents some experimental results on the realization of a parallel simulation of an Arti...
Obtaining optimal solutions for engineering design problems is often expensive because the process t...
Parallelism and distribution have been considered the key features of neural processing. The term pa...
Over the last few years tremendous progress has been made in neuroscience by employing simulation to...
Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applyi...
In this paper we describe the design, development, and performance of a neural network simulator for...
Contains fulltext : 18657.pdf (publisher's version ) (Open Access)In this thesis, ...
While conventional computers must be programmed in a logical fashion by a person who thoroughly unde...
Over the last few years tremendous progress has been made in neuroscience by employing simulation to...
The Back Propagation Model for neural network simulation is a very simple and very popular model for...
Artificial neural networks are systems composed of interconnected simple computing units known as a...
Neural networks have attracted much interest recently, and using parallel architectures to simulate ...
This paper describes the specification and implementation of PANNS (Parallel Artifical Neural Networ...
This thesis is about parallelizing the training phase of a feed-forward, artificial neural network....
Neural networks tend to fall into two general categories: (1) software simulations, or (2) custom ha...
This paper presents some experimental results on the realization of a parallel simulation of an Arti...
Obtaining optimal solutions for engineering design problems is often expensive because the process t...
Parallelism and distribution have been considered the key features of neural processing. The term pa...
Over the last few years tremendous progress has been made in neuroscience by employing simulation to...