Deep reinforcement learning processor design for mobile applications / Juhyoung Lee, Hoi-Jun Yoo.
2023
Q325.73 .L44 2023
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Details
Title
Deep reinforcement learning processor design for mobile applications / Juhyoung Lee, Hoi-Jun Yoo.
Author
Lee, Juhyoung, author.
ISBN
9783031367939 (electronic bk.)
3031367936 (electronic bk.)
9783031367922
3031367928
3031367936 (electronic bk.)
9783031367922
3031367928
Published
Cham : Springer, [2023]
Language
English
Description
1 online resource (vi, 101 pages) : illustrations (some color)
Item Number
10.1007/978-3-031-36793-9 doi
Call Number
Q325.73 .L44 2023
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed August 24, 2023).
Added Author
Yoo, Hoi-Jun, author.
Available in Other Form
Print version: 9783031367922
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Table of Contents
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.
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.