The Institution of Engineering and Technology, 2020 eISBN: 978-1-78561-769-0 | Cloth: 978-1-78561-768-3
ABOUT THIS BOOK | TOC
ABOUT THIS BOOK
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.
TABLE OF CONTENTS
Part I: Deep learning and neural networks: concepts and models
Chapter 1: An introduction to artificial neural networks
Chapter 2: Hardware acceleration for recurrent neural networks
Chapter 3: Feedforward neural networks on massively parallel architectures
Part II: Deep learning and approximate data representation
Chapter 4: Stochastic-binary convolutional neural networks with deterministic bit-streams
Chapter 5: Binary neural networks
Part III: Deep learning and model sparsity
Chapter 6: Hardware and software techniques for sparse deep neural networks
Chapter 7: Computation reuse-aware accelerator for neural networks
Part IV: Convolutional neural networks for embedded systems
Chapter 8: CNN agnostic accelerator design for low latency inference on FPGAs
Chapter 9: Iterative convolutional neural network (ICNN): an iterative CNN solution for low power and real-time systems
Part V: Deep learning on analog accelerators
Chapter 10: Mixed-signal neuromorphic platform design for streaming biomedical signal processing