Moons, Bert.
Embedded Deep Learning Algorithms, Architectures and Circuits for Always-on Neural Network Processing / [electronic resource] : by Bert Moons, Daniel Bankman, Marian Verhelst. - 1st ed. 2019. - XVI, 206 p. 124 illus., 92 illus. in color. online resource.
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
Electronic circuits.
Signal processing.
Electronics.
Electronic Circuits and Systems.
Signal, Speech and Image Processing .
Electronics and Microelectronics, Instrumentation.
TK7867-7867.5
621.3815
Embedded Deep Learning Algorithms, Architectures and Circuits for Always-on Neural Network Processing / [electronic resource] : by Bert Moons, Daniel Bankman, Marian Verhelst. - 1st ed. 2019. - XVI, 206 p. 124 illus., 92 illus. in color. online resource.
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
Electronic circuits.
Signal processing.
Electronics.
Electronic Circuits and Systems.
Signal, Speech and Image Processing .
Electronics and Microelectronics, Instrumentation.
TK7867-7867.5
621.3815