{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:11:49Z","timestamp":1774631509274,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Model Recovery (MR) enables safe, explainable decision-making in mission-critical autonomous systems (MCAS) by learning governing dynamical equations, but its deployment on edge devices is hindered by the iterative nature of neural ordinary differential equations (NODE), which are inefficient on FPGAs. Memory and energy consumption are the main concern of applying MR on edge devices for real-time running MR. We propose MERINDA, a novel FPGA-accelerated MR framework that replaces iterative solvers with a parallelizable neural architecture equivalent to NODEs. MERINDA achieves nearly 11\u00d7 lower DRAM usage and 2.2\u00d7 faster runtime compared to mobile GPUs. Experiments reveal an inverse relationship between memory and energy at fixed accuracy, highlighting MERINDA\u2019s suitability for resource-constrained, real-time MCAS. \u201cThe implementation and datasets are publicly available at github.com\/ImpactLabASU\/ECAI2025.\u201d<\/jats:p>","DOI":"10.3233\/faia251275","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:45Z","timestamp":1761127005000},"source":"Crossref","is-referenced-by-count":4,"title":["Model Recovery at the Edge Under Resource Constraints for Physical AI"],"prefix":"10.3233","author":[{"given":"Bin","family":"Xu","sequence":"first","affiliation":[{"name":"IMPACT lab, Arizona State University, Tempe, AZ, USA. Email: binxu4@asu.edu, Ayan.Banerjee@asu.edu, Sandeep.Gupta@asu.edu"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6529-1644","authenticated-orcid":false,"given":"Ayan","family":"Banerjee","sequence":"additional","affiliation":[{"name":"IMPACT lab, Arizona State University, Tempe, AZ, USA. Email: binxu4@asu.edu, Ayan.Banerjee@asu.edu, Sandeep.Gupta@asu.edu"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6108-5584","authenticated-orcid":false,"given":"Sandeep K.S.","family":"Gupta","sequence":"additional","affiliation":[{"name":"IMPACT lab, Arizona State University, Tempe, AZ, USA. Email: binxu4@asu.edu, Ayan.Banerjee@asu.edu, Sandeep.Gupta@asu.edu"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:45Z","timestamp":1761127005000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251275","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}