Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Hardware Architectures for Deep Learning

Hardware Architectures for Deep Learning

Daneshtalab, Masoud
Modarressi, Mehdi

Hardcover

Series: Materials, Circuits and Devices

General Computers

ISBN10: 1785617680
ISBN13: 9781785617683
Publisher: Institution Of Engineering & T
Published: Apr 24 2020
Pages: 328
Weight: 1.50
Height: 0.80 Width: 6.30 Depth: 9.40
Language: English

This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.

The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency.

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