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Showing results 1 to 5 of 5
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  • An Energy-Efficient Programmable Mixed-Signal Accelerator for Machine Learning Algorithms

    • Authors: Kang, M.;  Advisor: -;  Participants: Srivastava, P.; Adve, V.; Kim, N. S.; Shanbhag, N. R. (2019)

  • We propose PROMISE, the first end-to-end design of a PROgrammable MIxedSignal accElerator from Instruction Set Architecture to high-level language compiler for acceleration of diverse machine learning algorithms by exploiting the advantage of the superior energy efficiency from analog/mixed-signal processing.
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  • Boosted unsupervised multi-source selection for domain adaption

    • Authors: Paul, A.; Vogt, K.;  Advisor: -;  Participants: - (2017)

  • Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training data to new (target) data sets. A problem in TL is how to quantify the relatedness of a source quickly and robustly, because transferring knowledge from unrelated data can degrade the performance of a classifier. In this paper, we propose a method that can select a nearly optimal source from a large number of candidate sources. This operation depends only on the marginal probability distributions of the data, thus allowing the use of the ofte...
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  • Semantic segmentation of settlement patterns in gray-scale map images using RF and CRF within an HPC environment

    • Authors: Schemala, D. de;  Advisor: -;  Participants: - (2016)

  • The segmentation of complex and compound geographical objects such as settlement areas in images is a challenging task. This is particularly true if the images are scanned historical maps, which are only available as gray-scale images. On the other hand, their vast availability and spatio-temporal coverage make these maps a valuable source of information for historical land use and land cover (LULC) research and urban geography. To facilitate the laborious information extraction from these maps, we present a twostage machine learning-based approach for segmenting settlement and non-settlement areas in the map scans. We employ a Conditional Random Field (CRF) which obtains its unary po...
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  • SSDStreamer: Specializing I/O Stack for Large-Scale Machine Learning

    • Authors: Bae, J.;  Advisor: -;  Participants: Jang, H.; Gong, J.; Jin, W.; Kim, S.; Jang, J.; Ham, T. J.; Jeong, J.; Lee, J. W. (2019)

  • Abstract—This article presents SSDStreamer, an SSD-based caching system for largescale machine learning. By using DRAM as stream buffer, instead of an upper-level cache, SSDStreamer significantly outperforms state-of-the-art multilevel caching systems on Apache Spark, while requiring much less DRAM capacity.
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  • Value of feature reduction for crop differentiation using multi-temporal imagery, machine learning, and object-based image analysis.

    • Authors: Gilbertson, J.K.;  Advisor: -;  Participants: - (2016)

  • This study examined the value of automated and manual feature selection, when applied to machine learning and object-based image analysis (OBIA), for the differentiation of crops in a Mediterranean climate. Five Landsat8 images covering the phenological stages of seven major crops types in the study area (Cape Winelands, South Africa) were acquired and processed. A statistical image fusion technique was used to enhance the spatial resolution of the imagery. The pan-sharpened imagery was used to produce a range of spectral features, textural measures, indices and colour transformations, after which it was segmented using the multi-resolution (MRS) algorithm. The entire set of 205 featu...