Sartin, Maicon A.Da Silva, Alexandre C.R. [UNESP]2014-05-272014-05-272013-09-162013 8th International Workshop on Reconfigurable and Communication-Centric Systems-on-Chip, ReCoSoC 2013.http://hdl.handle.net/11449/76564Artificial 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.engactivation functionFPGAHybrid Methodshyperbolic tangentActivation functionsHybrid methodHyperbolic tangentNonlinear activation functionsNonlinear functionsNonlinear problemsReconfigurable devicesSystem architecturesCommunicationField programmable gate arrays (FPGA)Hyperbolic functionsNeural networksReconfigurable hardwareTrabalho apresentado em evento10.1109/ReCoSoC.2013.6581545Acesso aberto2-s2.0-84883659156