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Reconfigurable Technol. Syst."],"published-print":{"date-parts":[[2017,3,31]]},"abstract":"<jats:p>\n            We propose SSketch, a novel automated framework for efficient analysis of dynamic big data with dense (non-sparse) correlation matrices on reconfigurable platforms. SSketch targets\n            <jats:italic>streaming<\/jats:italic>\n            applications where each data sample can be processed only once and storage is severely limited. Our framework adaptively learns from the stream of input data and updates a corresponding ensemble of lower-dimensional data structures, a.k.a., a\n            <jats:italic>sketch matrix<\/jats:italic>\n            . A new sketching methodology is introduced that tailors the problem of transforming the big data with dense correlations to an ensemble of lower-dimensional subspaces such that it is suitable for hardware-based acceleration performed by reconfigurable hardware. The new method is scalable, while it significantly reduces costly memory interactions and enhances matrix computation performance by leveraging coarse-grained parallelism existing in the dataset. SSketch provides an automated optimization methodology for creating the most accurate data sketch for a given set of user-defined constraints, including runtime and power as well as platform constraints such as memory. To facilitate automation, SSketch takes advantage of a Hardware\/Software (HW\/SW) co-design approach: It provides an Application Programming Interface that can be customized for rapid prototyping of an arbitrary matrix-based data analysis algorithm. Proof-of-concept evaluations on a variety of visual datasets with more than 11 million non-zeros demonstrate up to a 200-fold speedup on our hardware-accelerated realization of SSketch compared to a software-based deployment on a general-purpose processor.\n          <\/jats:p>","DOI":"10.1145\/2974023","type":"journal-article","created":{"date-parts":[[2016,12,19]],"date-time":"2016-12-19T17:06:31Z","timestamp":1482167191000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Real-Time Analysis of Streaming Big and Dense Data on Reconfigurable Platforms"],"prefix":"10.1145","volume":"10","author":[{"given":"Bita Darvish","family":"Rouhani","sequence":"first","affiliation":[{"name":"UC San Diego, La Jolla, CA"}]},{"given":"Azalia","family":"Mirhoseini","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX"}]},{"given":"Ebrahim M.","family":"Songhori","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX"}]},{"given":"Farinaz","family":"Koushanfar","sequence":"additional","affiliation":[{"name":"UC San Diego, La Jolla, CA"}]}],"member":"320","published-online":{"date-parts":[[2016,12,19]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Mircea Andrecut. 2008. 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