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Deep reinforcement learning processo...

Lee, Juhyoung.

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Deep reinforcement learning processor design for mobile applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep reinforcement learning processor design for mobile applications/ by Juhyoung Lee, Hoi-Jun Yoo.
作者:	Lee, Juhyoung.
其他作者:	Yoo, Hoi-Jun.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	vi, 101 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning) -
電子資源:	https://doi.org/10.1007/978-3-031-36793-9
ISBN:	9783031367939

Deep reinforcement learning processor design for mobile applications
Lee, Juhyoung.

Deep reinforcement learning processor design for mobile applications[electronic resource] /by Juhyoung Lee, Hoi-Jun Yoo. - Cham :Springer Nature Switzerland :2023. - vi, 101 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783031367939

Standard No.: 10.1007/978-3-031-36793-9doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep reinforcement learning processor design for mobile applications
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520 $a 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.
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