Approximation of Hyperbolic Tangent Activation Function Using Hybrid Methods
Loading...
Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee
Type
Work presented at event
Access right
Abstract
Artificial Neural Networks are widely used in various applications in engineering, as such solutions of nonlinear problems. The implementation of this technique in reconfigurable devices is a great challenge to researchers by several factors, such as floating point precision, nonlinear activation function, performance and area used in FPGA. The contribution of this work is the approximation of a nonlinear function used in ANN, the popular hyperbolic tangent activation function. The system architecture is composed of several scenarios that provide a tradeoff of performance, precision and area used in FPGA. The results are compared in different scenarios and with current literature on error analysis, area and system performance.
Description
Language
English
Citation
2013 8th International Workshop On Reconfigurable And Communication-centric Systems-on-chip (recosoc). New York: Ieee, 6 p., 2013.
