GPU implementation of neural networks

By Kyoungsu Oh, K. Jung. 2004

445 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “LSTM also worked on tasks involving context free languages (CFLs) that cannot be represented by HMMs or similar FSAs discussed in the RNN literature (Andrews, Diederich, & Tickle, 1995; Rodriguez & Wiles, 1998; Rodriguez, Wiles, & Elman, 1999; Steijvers & Grunwald, 1996; Sun, Giles, Chen, & Lee,…”
Unmasking Clever Hans predictors and assessing what machines really learn (2019, background): “The advent of GPU accelerated training [87] and the availability of performant deep learning frameworks [88–92], as well as large datasets [33, 93–97] lead to unprecedented accuracy in a variety of tasks [5, 7, 78, 80, 98, 99].”
The hardware lottery (2020, methodology): “In the 2000s, like Edison’s phonograph, GPUs were re-purposed for an entirely unimag-ined use case – to train deep neural networks (Chellapilla et al., 2006; Oh & Jung, 2004; Claudiu Ciresan et al., 2010; Fata-halian et al., 2004; Payne et al., 2005).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “Today’s faster computers, such as GPUs, mitigate the Fundamental Deep Learning Problem for FNNs [181, 34, 198, 38, 40].”
RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning (2019, methodology): “All models were trained on Nvidia GTX TITAN X graphics processing unit (GPU) to speed up training 65 .”
Restructuring of deep neural network acoustic models with singular value decomposition (2013, methodology): “Also, GPU is not always available on all types of hardware, especially for some small devices, which limits the application of DNN in a lot of scenarios.”
Deep learning in robotics: a review of recent research (2017, background): “To make effective use of deep learning models, it is important to train on one or more general purpose graphical processing units (GPGPUs) [17].”
A Deep Learning Model for Robust Wafer Fault Monitoring With Sensor Measurement Noise (2017, methodology): “Srivastava et al. [20] proposed the dropout method to prevent overfitting, and Oh and Jung [21] suggested a method of increasing the learning rates of artificial neural networks by employing graphical processing units.”
A Survey on Distributed Machine Learning (2019, methodology): “Although the x86_64 and ARM processor architectures are mainstream to execute applications in the server and mobile devices market respectively, we witness a shift towards using GPU hardware for e � ciently executing neural network models [109].”
Deep Learning with Dynamic Computation Graphs (2017, background): “These successes exploit the effectiveness of training via gradient descent on mini-batches of tens to hundreds of inputs, implemented using the parallel SIMD capabilities of modern GPUs (Oh & Jung, 2004) and multi-core CPUs (Vanhoucke et al., 2011).”
A survey of the recent architectures of deep convolutional neural networks (2019, methodology): “sing capabilities of GPU with a greater degree [94], [95]. In essence, the use of GPUs for NN and CNN training and other hardware improvements were the main factors, which revived the research in CNN [92], [96]. In 2010, Fei-Fei Li’s group at Stanford, established a large database of images known as”
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017, methodology): “The modern efficient form of the algorithm that addressed these issues was derived in 1970 (Linnainmaa, 1970); however, there was no mention of its use for ANNs.”
Neurovisual Control in the Quake II Environment (2012, background): “NNs are also able to take advantage of multiple processors, spreading out their computations in parallel much like the human visual system, and can even compute using the hundreds of cores in graphics processing units [11].”