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

Format:

Book

Author/Creator:

Moons, Bert, author.

Bankman, Daniel, author.

Verhelst, Marian, author.

Contributor:

SpringerLink (Online service)

Series:

Engineering (Springer-11647)

Language:

English

Subjects (All):

Electronic circuits.

Signal processing.

Image processing.

Speech processing systems.

Electronics.

Microelectronics.

Circuits and Systems.

Signal, Image and Speech Processing.

Electronics and Microelectronics, Instrumentation.

Local Subjects:

Circuits and Systems.

Signal, Image and Speech Processing.

Electronics and Microelectronics, Instrumentation.

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.