Electronic Resource

Embedded Deep Learning

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Penilaian

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This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.

Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;
Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes;
Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;
Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1908001915	EB0002576	370.152 3 Moo e	Central Library	Available

Detail Information

Series Title

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Call Number

370.152 3 Moo e

Publisher

Switzerland : Springer Cham., 2018

Collation

XVI, 206p.:Ill

Language

English

ISBN/ISSN

978-3-319-99223-5

Classification

370.152 3

Content Type

Ebook

Media Type

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Carrier Type

online resource

Edition

1

Subject(s)

Motivation in Education

Specific Detail Info

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Statement of Responsibility

BRF

Other version/related

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