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Johann Hauswald, Yiping Kang, Michael A. Laurenzano, Quan Chen, Cheng Li, Trevor Mudge, Ronald G. Dreslinski, Jason Mars, Lingjia Tang
As applications such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, webservice companies are adopting large deep neural networks (DNN) for machine learning challenges such as image processing, speech recognition, natural language processing, among others. A number of open questions arise as to the design of a server platform […]
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Fatemah Ramzy AlZayer
We optimize parameters in OpenACC clauses for a stencil evaluation kernel executed on Graphical Processing Units (GPUs) using a variety of machine learning and optimization search algorithms, individually and in hybrid combinations, and compare execution time performance to the best possible obtained from brute force search. Several auto-tuning techniques – historic learning, random walk, simulated […]
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Guy L. Steele Jr. (Oracle Labs), Jean-Baptiste Tristan
We describe a technique for drawing values from discrete distributions, such as sampling from the random variables of a mixture model, that avoids computing a complete table of partial sums of the relative probabilities. A table of alternate ("butterfly-patterned") form is faster to compute, making better use of coalesced memory accesses. From this table, complete […]
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Tobias Domhan, Jost Tobias Springenberg, Frank Hutter
Deep neural networks (DNNs) show very strong performance on many machine learning problems, but they are very sensitive to the setting of their hyperparameters. Automated hyperparameter optimization methods have recently been shown to yield settings competitive with those found by human experts, but their widespread adoption is hampered by the fact that they require more […]
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Michael Figurnov, Dmitry Vetrov, Pushmeet Kohli
This paper proposes a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in low-power devices such as mobile phones. Our method is inspired by the loop perforation technique from source code optimization and accelerates the evaluation of bottleneck convolutional layers by exploiting the […]
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Firas Abuzaid, Stefan Hadjis, Ce Zhang, Christopher Re
We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural networks across different hardware architectures. We find that, by employing standard batching optimizations for CPU training, we achieve up to one […]
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Keven (Kedao) Wang
This project classifies images in Tiny ImageNet Challenge, a dataset with 200 classes and 500 training examples for each class. Three network architectures are experimented: a traditional architecture with 4 convolutional layers + 2 fully-connected layers; a Tiny GoogleNet with 3 inception layers; and a pyramid representation-based network. Tiny GoogleNet achieved the highest top-1 validation […]
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Aanchan Mohan, Richard Rose
Multi-task learning (MTL) for deep neural network (DNN) multilingual acoustic models has been shown to be effective for learning parameters that are common or shared between multiple languages[1, 2]. In the MTL paradigm, the number of parameters in the output layer is large and scales with the number of languages used in training. This output […]
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Dayong Wang, Anil K. Jain
Face retrieval is an enabling technology for many applications, including automatic face annotation, deduplication, and surveillance. In this paper, we propose a face retrieval system which combines a k-NN search procedure with a COTS matcher (PittPatt) in a cascaded manner. In particular, given a query face, we first pre-filter the gallery set and find the […]
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Kyuyeon Hwang, Wonyong Sung
Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data. However, they suffer from long training time, which demands parallel implementations of the training procedure. Parallelization of the training algorithms for RNNs are very challenging because internal recurrent paths form dependencies between two different time frames. In this paper, we first propose a […]
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Ken Miura, Tatsuya Harada
Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing […]
Zheng Yi Wu, Mahmoud Elmaghraby
Artificial neural network (ANN) is widely applied as the data-driven modeling tool in hydroinformatics due to its broad applicability of handling implicit and nonlinear relationships between the input and output data. To obtain a reliable ANN model, training ANN using the data is essential, but the training is usually taking many hours for a large […]
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