Accelerating Deep Reinforcement Learning via Phase-Level Parallelism for Robotics Applications

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dc.contributor.authorKim, Yang-Gonko
dc.contributor.authorHan, Yunkiko
dc.contributor.authorShin, Jaekangko
dc.contributor.authorKim, Jun-Kyumko
dc.contributor.authorKim, Lee-Supko
dc.date.accessioned2024-08-29T11:00:14Z-
dc.date.available2024-08-29T11:00:14Z-
dc.date.created2024-08-29-
dc.date.issued2024-01-
dc.identifier.citationIEEE COMPUTER ARCHITECTURE LETTERS, v.23, no.1, pp.41 - 44-
dc.identifier.issn1556-6056-
dc.identifier.urihttp://hdl.handle.net/10203/322474-
dc.description.abstractDeep Reinforcement Learning (DRL) plays a critical role in controlling future intelligent machines like robots and drones. Constantly retrained by newly arriving real-world data, DRL provides optimal autonomous control solutions for adapting to ever-changing environments. However, DRL repeats inference and training that are computationally expensive on resource-constraint mobile/embedded platforms. Even worse, DRL produces a severe hardware underutilization problem due to its unique execution pattern. To overcome the inefficiency of DRL, we propose Train Early Start, a new execution pattern for building the efficient DRL algorithm. Train Early Start parallelizes the inference and training execution, hiding the serialized performance bottleneck and improving the hardware utilization dramatically. Compared to the state-of-the-art mobile SoC, Train Early Start achieves 1.42x speedup and 1.13x energy efficiency.-
dc.languageEnglish-
dc.publisherIEEE COMPUTER SOC-
dc.titleAccelerating Deep Reinforcement Learning via Phase-Level Parallelism for Robotics Applications-
dc.typeArticle-
dc.identifier.wosid001181434900002-
dc.identifier.scopusid2-s2.0-85180291179-
dc.type.rimsART-
dc.citation.volume23-
dc.citation.issue1-
dc.citation.beginningpage41-
dc.citation.endingpage44-
dc.citation.publicationnameIEEE COMPUTER ARCHITECTURE LETTERS-
dc.identifier.doi10.1109/LCA.2023.3341152-
dc.contributor.localauthorKim, Lee-Sup-
dc.contributor.nonIdAuthorKim, Yang-Gon-
dc.contributor.nonIdAuthorKim, Jun-Kyum-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorGraphics processing units-
dc.subject.keywordAuthorRobots-
dc.subject.keywordAuthorTraining-
dc.subject.keywordAuthorLegged locomotion-
dc.subject.keywordAuthorBackpropagation-
dc.subject.keywordAuthorReinforcement learning-
dc.subject.keywordAuthorHardware-
dc.subject.keywordAuthorComputer systems organization-
dc.subject.keywordAuthorneural nets-
dc.subject.keywordAuthormobile computing-
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