Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Moons, Bert
Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing Bert Moons, Daniel Bankman, Marian Verhelst - 1 recurso en línea (XVI, 206 páginas) 124 ilustraciones - Engineering (Springer-11647) .
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
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
9783319992235
10.1007/978-3-319-99223-5 doi
Motivación en educación
Estrategias de aprendizaje
Educación--Proceso de datos
LB1065 / 2019 EB
Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing Bert Moons, Daniel Bankman, Marian Verhelst - 1 recurso en línea (XVI, 206 páginas) 124 ilustraciones - Engineering (Springer-11647) .
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
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
9783319992235
10.1007/978-3-319-99223-5 doi
Motivación en educación
Estrategias de aprendizaje
Educación--Proceso de datos
LB1065 / 2019 EB