{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:46:16Z","timestamp":1772909176365,"version":"3.50.1"},"reference-count":100,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01HL144683"],"award-info":[{"award-number":["R01HL144683"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["CCF-1838873"],"award-info":[{"award-number":["CCF-1838873"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>\n            There exists a gap in terms of the signals provided by pacemakers (i.e., intracardiac electrogram (EGM)) and the signals doctors use (i.e., 12-lead electrocardiogram (ECG)) to diagnose abnormal rhythms. Therefore, the former, even if remotely transmitted, are not sufficient for doctors to provide a precise diagnosis, let alone make a timely intervention. To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2) corresponding accelerators, to enable\n            <jats:bold>R<\/jats:bold>\n            eal-\n            <jats:bold>T<\/jats:bold>\n            ime and high-quality\n            <jats:bold>R<\/jats:bold>\n            econstruction of E\n            <jats:bold>C<\/jats:bold>\n            G signals from E\n            <jats:bold>G<\/jats:bold>\n            M signals. Specifically, RT-RCG proposes a new DNN search space tailored for ECG reconstruction from EGM signals and incorporates a differentiable acceleration search (DAS) engine to efficiently navigate over the large and discrete accelerator design space to generate optimized accelerators. Extensive experiments and ablation studies under various settings consistently validate the effectiveness of our RT-RCG. To the best of our knowledge, RT-RCG is the first to leverage neural architecture search (NAS) to simultaneously tackle both reconstruction efficacy and efficiency.\n          <\/jats:p>","DOI":"10.1145\/3465372","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T07:49:16Z","timestamp":1647416956000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms"],"prefix":"10.1145","volume":"18","author":[{"given":"Yongan","family":"Zhang","sequence":"first","affiliation":[{"name":"Rice University, Houston, TX, USA"}]},{"given":"Anton","family":"Banta","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}]},{"given":"Yonggan","family":"Fu","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}]},{"given":"Mathews M.","family":"John","sequence":"additional","affiliation":[{"name":"Texas Heart Institute, Houston, TX, USA"}]},{"given":"Allison","family":"Post","sequence":"additional","affiliation":[{"name":"Texas Heart Institute, Houston, TX, USA"}]},{"given":"Mehdi","family":"Razavi","sequence":"additional","affiliation":[{"name":"Texas Heart Institute, Houston, TX, USA"}]},{"given":"Joseph","family":"Cavallaro","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}]},{"given":"Behnaam","family":"Aazhang","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}]},{"given":"Yingyan","family":"Lin","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Best of both worlds: AutoML codesign of a CNN and its hardware accelerator","author":"Abdelfattah Mohamed S.","year":"2020","unstructured":"Mohamed S. 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