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Title: FPGA implementation of neurocomputational models: comparison between standard back-propagation and C-Mantec constructive algorithm
Authors: Ortega Zamorano, Francisco 
Jerez, José M.
Juárez, Gustavo E.
Franco, Leonardo
UNESCO Clasification: 1203 Ciencia de los ordenadores
Keywords: Constructive neural networks
Hardware implementation
Issue Date: 2017
Journal: Neural Processing Letters 
Abstract: Recent advances in FPGA technology have permitted the implementation of neurocomputational models, making them an interesting alternative to standard PCs in order to speed up the computations involved taking advantage of the intrinsic FPGA parallelism. In this work, we analyse and compare the FPGA implementation of two neural network learning algorithms: the standard and well known Back-Propagation algorithm and C-Mantec, a constructive neural network algorithm that generates compact one hidden layer architectures with good predictive capabilities. One of the main differences between both algorithms is the fact that while Back-Propagation needs a predefined architecture, C-Mantec constructs its network while learning the input patterns. Several aspects of the FPGA implementation of both algorithms are analyzed, focusing in features like logic and memory resources needed, transfer function implementation, computation time, etc. The advantages and disadvantages of both methods in relationship to their hardware implementations are discussed.
ISSN: 1370-4621
DOI: 10.1007/s11063-017-9655-x
Source: Neural Processing Letters [ISSN 1370-4621], n. 46, p. 899-914
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