Open AccessJournal Article
Neural Network Implementation Using FPGA: Issues and Application
TLDR
The issues involved in implementation of a multi-input neuron with linear/nonlinear excitation functions using FPGA, and the proposed method of implementation a neural network based application, namely, a Space vector modulator for a vector-controlled drive is presented.Abstract:
Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. This paper discusses the issues involved in implementation of a multi-input neuron with linear/nonlinear excitation functions using FPGA. Implementation method with resource/speed tradeoff is proposed to handle signed decimal numbers. The VHDL coding developed is tested using Xilinx XC V50hq240 Chip. To improve the speed of operation a lookup table method is used. The problems involved in using a lookup table (LUT) for a nonlinear function is discussed. The percentage saving in resource and the improvement in speed with an LUT for a neuron is reported. An attempt is also made to derive a generalized formula for a multi-input neuron that facilitates to estimate approximately the total resource requirement and speed achievable for a given multilayer neural network. This facilitates the designer to choose the FPGA capacity for a given application. Using the proposed method of implementation a neural network based application, namely, a Space vector modulator for a vector-controlled drive is presented Keywords— FPGA Implementation, Multi-input Neuron, Neural Network, NN based Space Vector Modulatorread more
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References
More filters
Book
Modern Power Electronics And Ac Drives
TL;DR: In this paper, the authors present a simulation of a six-step Thyristor Inverter with three-level Inverters and three-phase Bridge Invergers. And they present a Neural Network in Identification and Control toolbox.
Journal ArticleDOI
Neocognitron: A neural network model for a mechanism of visual pattern recognition
TL;DR: In this article, a large-scale network with a learning-with-a-teacher (L2Teacher) process is used for reinforcement of the modifiable synapses in the new large-size model, instead of the learning-without-a teacher process applied to a previous model.
Book
Neural nets for adaptive filtering and adaptive pattern recognition
Bernard Widrow,Rodney Winter +1 more
TL;DR: The adaptive linear combiner (ALC) as mentioned in this paper was proposed for signal processing and pattern recognition, and practical applications of the ALC in signal processing were described. But it was not used for pattern recognition.
Journal ArticleDOI
Neural nets for adaptive filtering and adaptive pattern recognition
Bernard Widrow,Rodney Winter +1 more
TL;DR: The adaptive linear combiner is described, and practical applications of the ALC in signal processing and pattern recognition are presented, and Adaptive pattern recognition using neural nets is discussed.
Journal ArticleDOI
Hardware Implementation of a Real-Time Neural Network Controller With a DSP and an FPGA for Nonlinear Systems
Seul Jung,Sung-Su Kim +1 more
TL;DR: The designed intelligent control hardware can perform real-time control of the backpropagation learning algorithm of a neural network and becomes cost effective by using a high capacity of an FPGA chip.
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