001476191 000__ 03712cam\\22006497i\4500
001476191 001__ 1476191
001476191 003__ OCoLC
001476191 005__ 20231003174637.0
001476191 006__ m\\\\\o\\d\\\\\\\\
001476191 007__ cr\un\nnnunnun
001476191 008__ 230824s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001476191 019__ $$a1394001407$$a1394117786
001476191 020__ $$a9783031367939$$q(electronic bk.)
001476191 020__ $$a3031367936$$q(electronic bk.)
001476191 020__ $$z9783031367922
001476191 020__ $$z3031367928
001476191 0247_ $$a10.1007/978-3-031-36793-9$$2doi
001476191 035__ $$aSP(OCoLC)1395079454
001476191 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCQ$$dOCLCO$$dYDX
001476191 049__ $$aISEA
001476191 050_4 $$aQ325.73$$b.L44 2023
001476191 08204 $$a006.3/1$$223/eng/20230824
001476191 1001_ $$aLee, Juhyoung,$$eauthor.
001476191 24510 $$aDeep reinforcement learning processor design for mobile applications /$$cJuhyoung Lee, Hoi-Jun Yoo.
001476191 264_1 $$aCham :$$bSpringer,$$c[2023]
001476191 300__ $$a1 online resource (vi, 101 pages) :$$billustrations (some color)
001476191 336__ $$atext$$btxt$$2rdacontent
001476191 337__ $$acomputer$$bc$$2rdamedia
001476191 338__ $$aonline resource$$bcr$$2rdacarrier
001476191 504__ $$aIncludes bibliographical references and index.
001476191 5050_ $$aIntroduction -- Background of Deep Reinforcement Learning -- Group-Sparse Training Algorithm for Accelerating Deep Reinforcement Learning -- An Energy-Efficient Deep Reinforcement Learning Processor Design -- Low-power Autonomous Adaptation System with Deep Reinforcement Learning -- Low-power Autonomous Adaptation System with Deep Reinforcement Learning -- Exponent-Computing-in-Memory for DNN Training Processor with Energy-Efficient Heterogeneous Floating-point Computing Architecture.
001476191 506__ $$aAccess limited to authorized users.
001476191 520__ $$aThis book discusses the acceleration of deep reinforcement learning (DRL), which may be the next step in the burst success of artificial intelligence (AI). The authors address acceleration systems which enable DRL on area-limited & battery-limited mobile devices. Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction. Enables deep reinforcement learning (DRL) optimization at algorithm-, architecture-, and circuit-levels of abstraction; Includes methodologies that can reduce the high cost of DRL; Uses analysis of computational workload characteristics of DRL in the context of acceleration.
001476191 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 24, 2023).
001476191 650_0 $$aDeep learning (Machine learning)
001476191 650_0 $$aReinforcement learning.
001476191 650_0 $$aMobile communication systems$$xTechnological innovations.
001476191 650_6 $$aApprentissage profond.
001476191 650_6 $$aApprentissage par renforcement (Intelligence artificielle)
001476191 650_6 $$aRadiocommunications mobiles$$xInnovations.
001476191 655_0 $$aElectronic books.
001476191 7001_ $$aYoo, Hoi-Jun,$$eauthor.
001476191 77608 $$iPrint version: $$z3031367928$$z9783031367922$$w(OCoLC)1381444912
001476191 852__ $$bebk
001476191 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-36793-9$$zOnline Access$$91397441.1
001476191 909CO $$ooai:library.usi.edu:1476191$$pGLOBAL_SET
001476191 980__ $$aBIB
001476191 980__ $$aEBOOK
001476191 982__ $$aEbook
001476191 983__ $$aOnline
001476191 994__ $$a92$$bISE