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Proceedings Paper

FPGA-based artificial neural network using CORDIC modules
Author(s): Albert A. Liddicoat; Lynne A. Slivovsky; Tim McLenegan; Don Heyer
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Paper Abstract

Artificial neural networks have been used in applications that require complex procedural algorithms and in systems which lack an analytical mathematic model. By designing a large network of computing nodes based on the artificial neuron model, new solutions can be developed for computational problems in fields such as image processing and speech recognition. Neural networks are inherently parallel since each neuron, or node, acts as an autonomous computational element. Artificial neural networks use a mathematical model for each node that processes information from other nodes in the same region. The information processing entails computing a weighted average computation followed by a nonlinear mathematical transformation. Some typical artificial neural network applications use the exponential function or trigonometric functions for the nonlinear transformation. Various simple artificial neural networks have been implemented using a processor to compute the output for each node sequentially. This approach uses sequential processing and does not take advantage of the parallelism of a complex artificial neural network. In this work a hardware-based approach is investigated for artificial neural network applications. A Field Programmable Gate Arrays (FPGAs) is used to implement an artificial neuron using hardware multipliers, adders and CORDIC functional units. In order to create a large scale artificial neural network, area efficient hardware units such as CORDIC units are needed. High performance and low cost bit serial CORDIC implementations are presented. Finally, the FPGA resources and the performance of a hardware-based artificial neuron are presented.

Paper Details

Date Published: 25 August 2006
PDF: 8 pages
Proc. SPIE 6313, Advanced Signal Processing Algorithms, Architectures, and Implementations XVI, 63130B (25 August 2006); doi: 10.1117/12.682529
Show Author Affiliations
Albert A. Liddicoat, California Polytechnic State Univ. (United States)
Lynne A. Slivovsky, California Polytechnic State Univ. (United States)
Tim McLenegan, California Polytechnic State Univ. (United States)
Don Heyer, California Polytechnic State Univ. (United States)

Published in SPIE Proceedings Vol. 6313:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVI
Franklin T. Luk, Editor(s)

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