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Embedded deep learning: algorithms, architectures and circuits for always-on neural network processing

Bert Moons, Daniel Bankman, Marian Verhelst (Springer Nature, 2019)

 Abstrak

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 realizations implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.

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 Metadata

Jenis Koleksi: eBooks
No. Panggil : e20508149
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Subjek :
Penerbitan : Switzerland: Springer Nature, 2019
Sumber Pengatalogan LibUI eng rda
Tipe Konten text
Tipe Media computer
Tipe Carrier online resource
Deskripsi Fisik
Tautan https://doi.org/10.1007/978-3-319-99223-5
  • Ketersediaan
  • Ulasan
No. Panggil No. Barkod Ketersediaan
e20508149 02-20-893809432 TERSEDIA
e20508149 TERSEDIA
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