Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing

Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing
By 作者: Bert Moons – Daniel Bankman – Marian Verhelst
ISBN-10 书号: 3319992228
ISBN-13 书号: 9783319992228
Edition 版本: 1st ed. 2019
Release Finelybook 出版日期: 2018-10-24
pages 页数: (206 )

$139.99

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.

Front Matter
1. Embedded Deep Neural Networks
2. Optimized Hierarchical Cascaded Processing
3. Hardware-Algorithm Co-optimizations
4. Circuit Techniques for Approximate Computing
5. ENVISION: Energy-Scalable Sparse Convolutional Neural Network Processing
6. BINAREYE: Digital and Mixed-Signal Always-On Binary Neural Network Processing
7. Conclusions, Contributions, and Future Work

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