{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:26:15Z","timestamp":1750220775909,"version":"3.41.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2020,8,21]],"date-time":"2020-08-21T00:00:00Z","timestamp":1597968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Center for Pervasive Communications and Computing"},{"name":"Public Authority for Applied Education and Training (PAAET) at Shuwaikh, Kuwait"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>\n            Deep neural networks are widely used in many artificial intelligence applications. They have demonstrated state-of-the-art accuracy on many artificial intelligence tasks. For this high accuracy to occur, deep neural networks require the right parameter values. This is achieved by a process known as\n            <jats:italic>training<\/jats:italic>\n            . The training of large amounts of data via many iterations comes at a high cost in regard to computation time and energy. Optimal resource allocation would therefore reduce the training time. TensorFlow, a computational graph library developed by Google, alleviates the development of neural network models and provides the means to train these networks. In this article, we propose\n            <jats:italic>Adaptive HTF-MPR<\/jats:italic>\n            to carry out the resource allocation, or mapping, on TensorFlow. Adaptive HTF-MPR searches for the best mapping in a hybrid approach. We applied the proposed methodology on two well-known image classifiers: VGG-16 and AlexNet. We also performed a full analysis of the solution space of MNIST Softmax. Our results demonstrate that Adaptive HTF-MPR outperforms the default homogeneous TensorFlow mapping. In addition to the speed up, Adaptive HTF-MPR can react to changes in the state of the system and adjust to an improved mapping.\n          <\/jats:p>","DOI":"10.1145\/3396949","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:36:50Z","timestamp":1594125410000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive HTF-MPR"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1535-7320","authenticated-orcid":false,"given":"Ahmad","family":"Albaqsami","sequence":"first","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam S.","family":"Hosseini","sequence":"additional","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masoomeh","family":"Jasemi","sequence":"additional","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nader","family":"Bagherzadeh","sequence":"additional","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_2_1_2_1","volume-title":"et\u00a0al","author":"Abadi Mart\u00edn","year":"2015","unstructured":"Mart\u00edn Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg Corrado , et\u00a0al . 2015 . TensorFlow: Large- Scale Machine Learning on Heterogeneous Systems. TensorFlow . Mart\u00edn Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg Corrado, et\u00a0al. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. TensorFlow."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , et\u00a0al. 2016 . TensorFlow: A system for large-scale machine learning . In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916) . 265--283. Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, et\u00a0al. 2016. TensorFlow: A system for large-scale machine learning. 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