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In many use cases, off-chip \u201dactive\u201d cooling solutions are considered prohibitive in such reduced form factors. Core frequency throttling by existing dynamic thermal management techniques often compromises the Quality-of-Service (QoS) and violates real-time deadlines. This necessitates the adoption of intelligent resource management that simultaneously manages both thermal and latency performance. Coupled with the complexity of modern heterogeneous multi-cores, the periodic application updates that cater to ever-changing user requirements often render model-driven thermal-aware resource allocation approaches unsuitable for heterogeneous multi-core systems. For such application-architecture scenarios, we propose a novel self-learning based resource manager using Reinforcement Learning that intelligently manipulates core frequencies and task set mappings to fulfill thermal and latency objectives. Our framework employs a data-driven system modeling technique using Gaussian Process Regression to enable efficient offline training of this learning-based resource manager to avoid challenges associated with initial online training. We evaluate the approach on a heterogeneous embedded CPU-GPU platform with real workloads and observe a significant reduction in peak operating temperature when compared to the default onboard frequency governor as well as other learning-based state-of-the-art approaches.<\/jats:p>","DOI":"10.1145\/3708890","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T10:07:03Z","timestamp":1734689223000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Harnessing Machine Learning in Dynamic Thermal Management in Embedded CPU-GPU Platforms"],"prefix":"10.1145","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3522-1391","authenticated-orcid":false,"given":"Srijeeta","family":"Maity","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3490-1395","authenticated-orcid":false,"given":"Anirban","family":"Majumder","sequence":"additional","affiliation":[{"name":"Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2360-8807","authenticated-orcid":false,"given":"Rudrajyoti","family":"Roy","sequence":"additional","affiliation":[{"name":"Electronics And Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0562-0594","authenticated-orcid":false,"given":"Ashish","family":"Hota","sequence":"additional","affiliation":[{"name":"Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9329-6389","authenticated-orcid":false,"given":"Soumyajit","family":"Dey","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India"}]}],"member":"320","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_1_2_2","DOI":"10.1109\/MCS.2003.1200252"},{"key":"e_1_3_1_3_2","volume-title":"Constrained Markov Decision Processes","author":"Altman E.","year":"1999","unstructured":"E. 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