Approximation of hyperbolic tangent activation function using hybrid methods
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
27/05/2014
27/05/2014
16/09/2013
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Resumo |
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. © 2013 IEEE. |
Identificador |
http://dx.doi.org/10.1109/ReCoSoC.2013.6581545 2013 8th International Workshop on Reconfigurable and Communication-Centric Systems-on-Chip, ReCoSoC 2013. http://hdl.handle.net/11449/76564 10.1109/ReCoSoC.2013.6581545 2-s2.0-84883659156 |
Idioma(s) |
eng |
Relação |
2013 8th International Workshop on Reconfigurable and Communication-Centric Systems-on-Chip, ReCoSoC 2013 |
Direitos |
closedAccess |
Palavras-Chave | #activation function #FPGA #Hybrid Methods #hyperbolic tangent #Activation functions #Hybrid method #Hyperbolic tangent #Nonlinear activation functions #Nonlinear functions #Nonlinear problems #Reconfigurable devices #System architectures #Communication #Field programmable gate arrays (FPGA) #Hyperbolic functions #Neural networks #Reconfigurable hardware |
Tipo |
info:eu-repo/semantics/conferencePaper |