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One of the most complex tasks in this sense is image registration, which finds the optimal geometric alignment among 3D image stacks and is widely employed in healthcare and robotics. Given the high computational demand of such a procedure, hardware accelerators are promising real-time and energy-efficient solutions, but they are complex to design and integrate within software pipelines. Therefore, this work presents an automation framework called\n            <jats:sc>Hephaestus<\/jats:sc>\n            that generates efficient 3D image registration pipelines combined with reconfigurable accelerators. Moreover, to alleviate the burden from the software, we codesign software-programmable accelerators that can adapt at run-time to the image volume dimensions.\n            <jats:sc>Hephaestus<\/jats:sc>\n            features a cross-platform abstraction layer that enables transparently high-performance and embedded systems deployment. However, given the computational complexity of 3D image registration, the embedded devices become a relevant and complex setting being constrained in memory; thus, they require further attention and tailoring of the accelerators and registration application to reach satisfactory results. Therefore, with\n            <jats:sc>Hephaestus<\/jats:sc>\n            , we also propose an approximation mechanism that enables such devices to perform the 3D image registration and even achieve, in some cases, the accuracy of the high-performance ones. Overall,\n            <jats:sc>Hephaestus<\/jats:sc>\n            demonstrates 1.85\u00d7 of maximum speedup, 2.35\u00d7 of efficiency improvement with respect to the State of the Art, a maximum speedup of 2.51\u00d7 and 2.76\u00d7 efficiency improvements against our software, while attaining state-of-the-art accuracy on 3D registrations.\n          <\/jats:p>","DOI":"10.1145\/3607928","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T13:33:18Z","timestamp":1694266398000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["<scp>Hephaestus<\/scp>\n            : Codesigning and Automating 3D Image Registration on Reconfigurable Architectures"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3025-8620","authenticated-orcid":false,"given":"Giuseppe","family":"Sorrentino","sequence":"first","affiliation":[{"name":"Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8991-1443","authenticated-orcid":false,"given":"Marco","family":"Venere","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5834-0812","authenticated-orcid":false,"given":"Davide","family":"Conficconi","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6967-5079","authenticated-orcid":false,"given":"Eleonora","family":"D\u2019Arnese","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9883-9693","authenticated-orcid":false,"given":"Marco Domenico","family":"Santambrogio","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Italy"}]}],"member":"320","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"AMD-Xilinx. 2021. 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