Embedded Deep Learning

Bert Moons, Daniel Bankman, Marian Verhelst

Informasi Dasar

71 kali
21.21.565
621.381
Buku - Elektronik (E-Book)
12b

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.

Subjek

ALGORITHMS
 

Katalog

Embedded Deep Learning
978-3-319-99223-5
xvi, 206p.: pdf file.; 8 MB
English

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

Bert Moons, Daniel Bankman, Marian Verhelst
Perorangan
 
 

Penerbit

Springer International Publishing
 
2019

Koleksi

Kompetensi

 

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