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In this article, we propose a low overhead framework based on compressive sensing (CS) to reduce data transfers up to 67% without affecting signal quality. CS has two important kernels: \u201csensing\u201d and \u201creconstruction.\u201d In this article, we focus on CS reconstruction is using orthogonal matching pursuit (OMP) algorithm. We implement the OMP CS reconstruction algorithm on a domain-specific PENC many-core platform and a low-power Jetson TK1 platform consisting of an ARM CPU and a K1 GPU. Detailed performance analysis of OMP algorithm on each platform suggests that the PENC many-core platform has 15\u00d7 and 18\u00d7 less energy consumption and 16\u00d7 and 8\u00d7 faster reconstruction time as compared to the low-power ARM CPU and K1 GPU, respectively. Furthermore, we implement the proposed CS-based framework on heterogeneous architecture, in which the PENC many-core architecture is used as an \u201caccelerator\u201d and processing is performed on the ARM CPU platform. For demonstration, we integrate the proposed CS-based framework with a hadoop MapReduce platform for a face detection application. The results show that the proposed CS-based framework with the PENC many-core as an accelerator achieves a 26.15% data storage\/transfer reduction, with an execution time and energy consumption overhead of 3.7% and 0.002%, respectively, for 5,000 image transfers. Compared to the CS-based framework implementation on the low-power Jetson TK1 ARM CPU+GPU platform, the PENC many-core implementation is 2.3\u00d7 faster for the image reconstruction part, while achieving 29% higher performance and 34% better energy efficiency for the complete face detection application on the Hadoop MapReduce platform.<\/jats:p>","DOI":"10.1145\/3092944","type":"journal-article","created":{"date-parts":[[2017,12,6]],"date-time":"2017-12-06T21:23:15Z","timestamp":1512595395000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Low Overhead CS-Based Heterogeneous Framework for Big Data Acceleration"],"prefix":"10.1145","volume":"17","author":[{"given":"Amey","family":"Kulkarni","sequence":"first","affiliation":[{"name":"University of Maryland, Baltimore County"}]},{"given":"Colin","family":"Shea","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County"}]},{"given":"Tahmid","family":"Abtahi","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County"}]},{"given":"Houman","family":"Homayoun","sequence":"additional","affiliation":[{"name":"George Mason University"}]},{"given":"Tinoosh","family":"Mohsenin","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County"}]}],"member":"320","published-online":{"date-parts":[[2017,12,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2016. 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