A Fast Modified CORDIC—Implementation of Radial Basis Neural Networks

Authors:
U. Meyer-Bäse;A. Meyer-Bäse;J. Mellott;F. Taylor
Affiliations:
Department of Electrical Engineering, University of Florida, Gainesville, FL 32611;Department of Electrical Engineering, University of Florida, Gainesville, FL 32611;Department of Electrical Engineering, University of Florida, Gainesville, FL 32611;Department of Electrical Engineering, University of Florida, Gainesville, FL 32611
Venue:
Journal of VLSI Signal Processing Systems
Year:
1998

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Abstract

This paper describes how the real-time bandwidth of a radialbasis neural network (RBNN) can be improved by the use of afield programmable gate array (FPGA). Accelerated performanceis gained by moving the time-consuming RBNN exponentialcalculations from a general purpose processor to a dedicatedFPGA that implements an optimized CORDIC-algorithm. Thedesign methodology is presented and illustrated with a speechrecognition application.