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Deep reinforcement learning processor design for mobile applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep reinforcement learning processor design for mobile applicationsby Juhyoung Lee, Hoi-Jun Yoo.
Author:	Lee, Juhyoung.
other author:	Yoo, Hoi-Jun.
Published:	Cham :Springer Nature Switzerland :2023.
Description:	vi, 101 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning)
Online resource:	https://doi.org/10.1007/978-3-031-36793-9
ISBN:	9783031367939$q(electronic bk.)

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$q(electronic bk.)

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

913129
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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505 0 $a 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.
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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650 2 4 $a Embedded Systems. $3 919564
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