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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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    Access pattern-aware data placement for hybrid DRAM/NVM
    (TUBITAKScientific and Technical Research Council Turkey, 2017) Department of Computer Engineering; Erten, Didem Unat; Faculty Member; Department of Computer Engineering; College of Engineering; 219274
    in recent years, increased interest in data-centric applications has led to an increasing demand for large capacity memory systems. Nonvolatile memory (NVM) technologies enable new opportunities in terms of process-scaling and energy consumption, and have become an attractive memory technology that serves as a secondary memory at low cost. However, NVM has certain disadvantages for write references, due to its high dynamic energy consumption for writes and low bandwidth compared to DRaM writes. in this paper, we propose an access-aware placement of objects in the application code for two types of memories. Given the desired power savings and acceptable performance loss, our placement algorithm suggests candidate variables for NVM. We present an evaluation of the proposed technique on two applications and study the energy and performance consequences of different placements.
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    Spontaneous smile intensity estimation by fusing saliency maps and convolutional neural networks
    (Spie-Soc Photo-Optical Instrumentation Engineers, 2019) Wei, Qinglan; Morency, Louis-Philippe; Sun, Bo; N/A; Bozkurt, Elif; PhD Student; Graduate School of Sciences and Engineering; N/A
    Smile intensity estimation plays important roles in applications such as affective disorder prediction, life satisfaction prediction, camera technique improvement, etc. In recent studies, many researchers applied only traditional features, such as local binary pattern and local phase quantization (LPQ) to represent smile intensity. To improve the performance of spontaneous smile intensity estimation, we introduce a feature set that combines the saliency map (SM)-based handcrafted feature and non-low-level convolutional neural network (CNN) features. We took advantage of the opponent-color characteristic of SMs and the multiple convolutional level features, which were assumed to be mutually complementary. Experiments were made on the Binghamton-Pittsburgh 4D (BP4D) database and Denver Intensity of Spontaneous Facial Action (DISFA) database. We set the local binary patterns on three orthogonal planes (LBPTOP) method as a baseline, and the experimental results show that the CNN features can better estimate smile intensity. Finally, through the proposed SM-LBPTOP feature fusion with the median- and high-level CNN features, we obtained the best result (52.08% on BP4D, 70.55% on DISFA), demonstrating our hypothesis is reasonable: the SM-based handcrafted feature is a good supplement to CNNs in spontaneous smile intensity estimation. (C) 2019 SPIE and IS&T