CUDA-accelerated genetic feedforward-ANN training for data mining

Neural PCA and Maximum Likelihood Hebbian Learning on the GPU

“…The GPGPU APIs have simplified the development of neural algorithms and ANNs for the graphics hardware significantly [10,16] and a variety of neurocomputing algorithms were ported to the GPUs [10,14,15,16,18,20,22,24]. The CUDA platform was used to achieve 46 to 63 times faster learning of a feedforward ANN by the backpropagation algorithm by Sierra-Canto et al [24] while Lopes and Ribeiro [14] reported a 10 to 40 faster implementation of the multiple backpropagation training of feedforward and multiple feedforward ANNs.…”

“…Ghuzva et al [10] presented a coarse-grained implementation of the multilayer perceptron (MLP) on the CUDA platform that operated a set of MLPs in parallel 50 times faster than a sequential CPU-based implementation. The training of a feedforward neural network by genetic algorithms was implemented on CUDA by Patulea et al [20] and it was 10 times faster than a sequential version of the same algorithm. An application of a GPU-powered ANN for speech recognition is due to Scanzio et al [22].…”

“…Modern many-core GPUs have been successfully used to accelerate a variety of meta-heuristics and bio-inspired algorithms [6,12,13] including different types of artificial neural networks [1,10,11,14,15,17,18,20,22,24]. To fully utilize the parallel hardware, the algorithms have to be carefully adapted to data-parallel architecture of the GPUs [21].…”

An improved CUDA-based hybrid metaheuristic for fast controller of an evolutionary robot

“…The training of EA-ANN on the graphic platform accelerator is ideal, due to the features of NVIDIA's compute unified device architecture (CUDA) which is a programming framework for the massively parallel GPUs, also to the architecture of GPU that contains thousands of independent floating-point units connected to on-board memory enabling high memory bandwidth, making this device perfect for providing significant speed-up to the intensive parallel applications (Patulea et al, 2014), by respecting memory access rules.…”