Two-Steps Implementation of Sigmoid Function for Artificial Neural Network in Field Programmable Gate Array
The complex equation of sigmoid function is one of the most difficult problems encountered for implementing the artificial neural network (ANN) into a field programmable gate array (FPGA). To overcome this problem, the combination of second order nonlinear function (SONF) and the differential look...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/24854/ http://umpir.ump.edu.my/id/eprint/24854/ http://umpir.ump.edu.my/id/eprint/24854/1/Two-Steps%20Implementation%20of%20Sigmoid%20Function%20for%20Artificial%20Neural%20Network%20in%20Field%20Programmable%20Gate%20Array.pdf |
| Summary: | The complex equation of sigmoid function is one of the most difficult problems encountered for implementing the
artificial neural network (ANN) into a field programmable gate array (FPGA). To overcome this problem, the combination of second order nonlinear function (SONF) and the differential lookup table (dLUT) has been proposed in this paper. By using this two-steps approach, the output accuracy achieved is ten times better than that of using only SONF and two times better than that of using conventional lookup table (LUT). Hence, this method can be used in various applications that required the implementation of the ANN into FPGA. |
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