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Jinhwan Park, Wonyong Sung
Deep neural networks (DNNs) demand a very large amount of computation and weight storage, and thus efficient implementation using special purpose hardware is highly desired. In this work, we have developed an FPGA based fixed-point DNN system using only on-chip memory not to access external DRAM. The execution time and energy consumption of the developed […]
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Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. […]
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Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally
State-of-the art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware can help the computation, fetching the weights from DRAM can be as much as two orders of magnitude […]
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Patrick O. Glauner
Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features […]
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Prateek Tandon
The confluence of the rapid growth in electronic data in recent years, and the renewed interest in domain-specific hardware accelerators presents exciting technical opportunities. Traditional scale-out solutions for processing the vast amounts of text data have been shown to be energy- and cost-inefficient. In contrast, custom hardware accelerators can provide higher throughputs, lower latencies, and […]
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Yifei Li
Measuring the similarity between two streamlines is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying and streamline clustering. This dissertation presents a novel streamline similarity measure inspired by the bag-of-features concept from computer vision. Different from other streamline similarity measures, the proposed one considers both the distribution […]
Andoni Azkarate Saiz
Deep neural networks have recently gained popularity for improving state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Modern award-winning architectures have systematically surpassed previous attempts […]
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Zdenek Buk
The paper presents application of OpenCLLink in Wolfram Mathematica to accelerate fully recurrent neural networks using GPU. We also show the idea of automatically generated parts of source code using SymbolicC.
Francisco Javier Ordonez, Daniel Roggen
Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. […]
Mohammed Abdelrahim
Unlike machines, humans and animals have very complex reasoning capability that allows them to adapt to changes in the naturally world, while computers tend to be very limited in that same aspect. What limits machines from becoming adaptable can span many topics, but of these attributes which would limit a machines ability to adapt is […]
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Mohit Shridhar
In particle physics, Higgs Boson to tau-tau decay signals are notoriously difficult to identify due to the presence of severe background noise generated by other decaying particles. Our approach uses neural networks to classify events as signals or background noise.
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Amund Tvei, Torbjorn Morland, Thomas Brox Rost
In this paper we present DeepLearningKit – an open source framework that supports using pre- trained deep learning models (convolutional neural networks) for iOS, OS X and tvOS. DeepLearningKit is developed in Metal in order to utilize the GPU efficiently and Swift for integration with applications, e.g. iOS-based mobile apps on iPhone/iPad, tvOS-based apps for […]
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