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Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing / by Bert Moons, Daniel Bankman, Marian Verhelst.

Springer Nature - Springer Engineering eBooks 2019 English International Available online

Springer Nature - Springer Engineering eBooks 2019 English International
Format:
Author/Creator:
Contributor:
Series:
Language:
English
Subjects (All):
Local Subjects:
Physical Description:
1 online resource (XVI, 206 pages) : 124 illustrations, 92 illustrations in color
Edition:
First edition 2019.
Contained In:
Springer eBooks
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2019.
System Details:
text file PDF
Summary:
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.
Contents:
  • Chapter 1 Embedded Deep Neural Networks
  • Chapter 2 Optimized Hierarchical Cascaded Processing
  • Chapter 3 Hardware-Algorithm Co-optimizations
  • Chapter 4 Circuit Techniques for Approximate Computing
  • Chapter 5 ENVISION: Energy-Scalable Sparse Convolutional Neural Network Processing
  • Chapter 6 BINAREYE: Digital and Mixed-signal Always-on Binary Neural Network Processing
  • Chapter 7 Conclusions, contributions and future work.
Other Format:
Printed edition:
ISBN:
  • 978-3-319-99223-5
  • 9783319992235
OCLC:
1096460888
Access Restriction:
Restricted for use by site license.

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