Abstract
In this study, Field Programmable Gate Array (FPGA) implementation of Conic Section Function Neural Network (CSFNN) for a classification problem focused on iris plant is presented. This work demonstrates for the first time to our knowledge, the feed-forward computation of CSFNN implementation on FPGA. Using 16-bit floating point arithmetic and the look-up tables (LUTs) for the sigmoid function and the square root function, 83% and 72% of slices and LUTs on Spartan 3-E XC3S1600E are used for the realization of CSFNN with five neurons. The classification results obtained from the FPGA implementation and software simulation show that the accuracy error between two platforms is only 0.1%.
Keywords
Field-programmable gate array
Computer science
Sigmoid function
Conic section
Artificial neural network
Realization (probability)
Programmable logic array
Field (mathematics)
Computer hardware
Programmable Array Logic
Gate array
Function (biology)
Floating point
Algorithm
Logic gate
Logic synthesis
Artificial intelligence
Mathematics
Logic family
Subject Areas
Neural Networks and Applications
·Artificial Intelligence ·Physical Sciences
Neural Networks and Reservoir Computing
·Artificial Intelligence ·Physical Sciences
Evolutionary Algorithms and Applications
·Artificial Intelligence ·Physical Sciences
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