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Embed. Comput. Syst."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this article, we introduce\n            <jats:sc>TinyNS<\/jats:sc>\n            , the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators.\n            <jats:sc>TinyNS<\/jats:sc>\n            provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models.\n            <jats:sc>TinyNS<\/jats:sc>\n            uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability,\n            <jats:sc>TinyNS<\/jats:sc>\n            talks to the target hardware during the optimization process. We showcase the utility of\n            <jats:sc>TinyNS<\/jats:sc>\n            by deploying microcontroller-class neurosymbolic models through several case studies. In all use cases,\n            <jats:sc>TinyNS<\/jats:sc>\n            outperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.\n          <\/jats:p>","DOI":"10.1145\/3603171","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T11:25:40Z","timestamp":1685532340000},"page":"1-48","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["TinyNS: Platform-aware Neurosymbolic Auto Tiny Machine Learning"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5357-2254","authenticated-orcid":false,"given":"Swapnil Sayan","family":"Saha","sequence":"first","affiliation":[{"name":"University of California-Los Angeles, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1421-1880","authenticated-orcid":false,"given":"Sandeep Singh","family":"Sandha","sequence":"additional","affiliation":[{"name":"Abacus.AI, Seattle, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1274-5408","authenticated-orcid":false,"given":"Mohit","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"BrightNight, Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8043-6944","authenticated-orcid":false,"given":"Brian","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California-Los Angeles, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9748-0597","authenticated-orcid":false,"given":"Liying","family":"Han","sequence":"additional","affiliation":[{"name":"University of California-Los Angeles, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1843-5830","authenticated-orcid":false,"given":"Julian De Gortari","family":"Briseno","sequence":"additional","affiliation":[{"name":"University of California-Los Angeles, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-9192","authenticated-orcid":false,"given":"Mani","family":"Srivastava","sequence":"additional","affiliation":[{"name":"University of California-Los Angeles, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"265","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, Geoffrey Irving, Michael Isard et\u00a0al. 2016. 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