Add to ORKGIn this paper, we present three different methods for implementing the Probabilistic Neural Network on a Beowulf cluster computer. The three methods, Parallel Full Training Set (PFT-PNN), Parallel Split Training Set (PST-PNN) and the Pipelined PNN (PPNN) all present different performance tradeoffs for different applications. We present implementations for all three architectures that are fully equivalent to the serial version and analyze the tradeoffs governing their potential use in actual engineering applications. Finally we provide performance results for all three methods on a Beowulf cluster. © 2006 IEEE
This paper addresses a simple way for neural network hardware implementation based on probabilistic ...
This paper presents an easy to use, constructive training algorithm for Probabilistic Neural Network...
A physical implementation of a non-volatile resistive switching device (ReRAM) and linking its conce...
In this paper, we present three different methods for implementing the Probabilistic Neural Network ...
It was pointed out in this paper that the planar topology of current backpropagation neural network ...
In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorit...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Feedforward neural networks are massively parallel computing structures that have the capability of ...
Probabilistic inference in belief networks is a promising technique for diagnosis, forecasting and d...
We present a technique for parallelizing the training of neural networks. Our technique is designed ...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
The modified probabilistic neural network for nonlinear time series analysis was developed and intro...
Probabilistic algorithms are computationally intensive approximate methods for solving intractable p...
Probabilistic algorithms are computationally intensive approximate methods for solving intractable p...
: Statistical Parallelism (SP), is new efficient method of parallel recalling from correlation matri...
This paper addresses a simple way for neural network hardware implementation based on probabilistic ...
This paper presents an easy to use, constructive training algorithm for Probabilistic Neural Network...
A physical implementation of a non-volatile resistive switching device (ReRAM) and linking its conce...
In this paper, we present three different methods for implementing the Probabilistic Neural Network ...
It was pointed out in this paper that the planar topology of current backpropagation neural network ...
In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorit...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Feedforward neural networks are massively parallel computing structures that have the capability of ...
Probabilistic inference in belief networks is a promising technique for diagnosis, forecasting and d...
We present a technique for parallelizing the training of neural networks. Our technique is designed ...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
The modified probabilistic neural network for nonlinear time series analysis was developed and intro...
Probabilistic algorithms are computationally intensive approximate methods for solving intractable p...
Probabilistic algorithms are computationally intensive approximate methods for solving intractable p...
: Statistical Parallelism (SP), is new efficient method of parallel recalling from correlation matri...
This paper addresses a simple way for neural network hardware implementation based on probabilistic ...
This paper presents an easy to use, constructive training algorithm for Probabilistic Neural Network...
A physical implementation of a non-volatile resistive switching device (ReRAM) and linking its conce...