Deep Reinforcement Learning Processor Design for Mobile Applications [[electronic resource] /] / by Juhyoung Lee, Hoi-Jun Yoo

• Autore: Lee Juhyoung
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (105 pages)
• Disciplina: 621.38456
• Soggetto topico: Electronic circuits; Embedded computer systems; Microprocessors; Computer architecture; Electronic Circuits and Systems; Embedded Systems; Processor Architectures
• Altri autori: YooHoi-Jun
• Nota di contenuto: Introduction -- 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.
• Sommario/riassunto: This 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.
• Titolo autorizzato: Deep Reinforcement Learning Processor Design for Mobile Applications
• ISBN: 3-031-36793-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910739463803321
• Lo trovi qui: Univ. Federico II