Title :
Feedforward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilization
Author :
Himavathi, S. ; Anitha, D. ; Muthuramalingam, A.
Author_Institution :
Electr. & Electron. Eng. Dept., Pondicherry Eng. Coll.
fDate :
5/1/2007 12:00:00 AM
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
Keywords :
feedforward neural nets; field programmable gate arrays; NN-based flux estimator; Xilinx FPGA XCV400hq240; control block; layer multiplexing; multilayer feedforward neural networks; reconfigurable field-programmable gate arrays; resource utilization; sequential processing; Application software; Artificial neural networks; Costs; Feedforward neural networks; Field programmable gate arrays; Multi-layer neural network; Neural network hardware; Neural networks; Parallel architectures; Resource management; Field-programmable gate array (FPGA); hardware implementation; layer multiplexing; neural networks (NNs); Algorithms; Artificial Intelligence; Computer Simulation; Equipment Design; Equipment Failure Analysis; Information Storage and Retrieval; Logistic Models; Neural Networks (Computer); Pattern Recognition, Automated; Signal Processing, Computer-Assisted;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891626