Hardware Architectures for Deep Learning

Hardware Architectures for Deep Learning
  • eBook:
    Hardware Architectures for Deep Learning (Materials, Circuits and Devices)
  • Author:
    Masoud Daneshtalab, Mehdi Modarressi
  • Edition:
  • Categories:
  • Data:
    April 24, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    328 pages
  • Format:
    PDF, ePUB

Book Description
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.
Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.


Part I - Deep learning and neural networks: concepts and models
1. An introduction to artificial neural networks
2. Hardware acceleration for recurrent neural networks
3. Feedforward neural networks on massively parallel architectures

Part II - Deep learning and approximate data representation
4. Stochastic-binary convolutional neural networks with deterministic bit-streams
5. Binary neural networks

Part III - Deep learning and model sparsity
6. Hardware and software techniques for sparse deep neural networks
7. Computation reuse-aware accelerator for neural networks

Part IV - Convolutional neural networks forembedded systems
8. CNN agnostic accelerator design for low latency inference on FPGAs
9. Iterative convolutional neural network (ICNN): an iterative CNN solution for low power and real-time systems

PartV - Deep learning on analog accelerators
10. Mixed-signal neuromorphic platform design for streaming biomedical signal processing
11. Inverter-based memristive neuromorphic circuit for ultra-low-power IoT smart applications

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