NDLI: Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization

Content Provider	IEEE Xplore Digital Library
Author	Himavathi, S. Anitha, D. Muthuramalingam, A.
Copyright Year	1990
Abstract	This paper presents a hardware implementation of multilayer feedforward neural networks (NN) using reconfigurable field-programmable gate arrays (FPGAs). Despite improvements in FPGA densities, the numerous multipliers in an NN limit the size of the network that can be implemented using a single FPGA, thus making NN applications not viable commercially. The proposed implementation is aimed at reducing resource requirement, without much compromise on the speed, so that a larger NN can be realized on a single chip at a lower cost. The sequential processing of the layers in an NN has been exploited in this paper to implement large NNs using a method of layer multiplexing. Instead of realizing a complete network, only the single largest layer is implemented. The same layer behaves as different layers with the help of a control block. The control block ensures proper functioning by assigning the appropriate inputs, weights, biases, and excitation function of the layer that is currently being computed. Multilayer networks have been implemented using Xilinx FPGA "XCV400hq240." The concept used is shown to be very effective in reducing resource requirements at the cost of a moderate overhead on speed. This implementation is proposed to make NN applications viable in terms of cost and speed for online applications. An NN-based flux estimator is implemented in FPGA and the results obtained are presented
Sponsorship	IEEE Computational Intelligence Society
Page Count	9
File Size	491285
Starting Page	880
Ending Page	888
File Format	PDF
ISSN	10459227
Volume Number	18
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Feedforward neural networks Field programmable gate arrays Resource management Multi-layer neural network Neural network hardware Costs Application software Artificial neural networks Parallel architectures neural networks (NNs) Field-programmable gate array (FPGA) hardware implementation layer multiplexing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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