Publicação: Approximation of hyperbolic tangent activation function using hybrid methods
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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.
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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
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2013 8th International Workshop on Reconfigurable and Communication-Centric Systems-on-Chip, ReCoSoC 2013.
