Experimental Platform for Accelerate the Training of ANNs with Genetic Algorithm and Embedded System on FPGA
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When implementing an artificial neural networks (ANNs) will need to know the topology and initial weights of each synaptic connection. The calculation of both variables is much more expensive computationally. This paper presents a scalable experimental platform to accelerate the training of ANN, using genetic algorithms and embedded systems with hardware accelerators implemented in FPGA (Field Programmable Gate Array). Getting a 3x-4x acceleration compared with Intel Xeon Quad-Core 2.83 Ghz and 6x-7x compared to AMD Optetron Quad-Core 2354 2.2Ghz.
KeywordsGenetic Algorithm Optimal Topology Embed System Convolutional Neural Network Experimental Platform
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