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Embedded Deep Learning

- Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Af: Daniel Bankman, Marian Verhelst, Bert Moons Engelsk Paperback

Embedded Deep Learning

- Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Af: Daniel Bankman, Marian Verhelst, Bert Moons Engelsk Paperback
Tjek vores konkurrenters priser

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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 206
ISBN-13: 9783030075774
Indbinding: Paperback
Udgave:
ISBN-10: 303007577X
Kategori: Elektronik: kredse og komponenter
Udg. Dato: 19 jan 2019
Længde: 15mm
Bredde: 233mm
Højde: 154mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 19 jan 2019
Forfatter(e): Daniel Bankman, Marian Verhelst, Bert Moons
Forfatter(e) Daniel Bankman, Marian Verhelst, Bert Moons

Kategori Elektronik: kredse og komponenter

ISBN-13 9783030075774

Sprog Engelsk

Indbinding Paperback

Sider 206

Udgave

Længde 15mm

Bredde 233mm

Højde 154mm

Udg. Dato 19 jan 2019

Oplagsdato 19 jan 2019

Forlag Springer Nature Switzerland AG

Kategori sammenhænge