Preface xi
Acknowledgment xix
1 Overview 1
1.1 History of Neural Networks 1
1.2 Neural Networks in Software 2
1.2.1 Artificial Neural Network 2
1.2.2 Spiking Neural Network 3
1.3 Need for Neuromorphic Hardware 3
1.4 Objectives and Outlines of the Book 5
2 Fundamentals and Learning of Artificial Neural Networks 11
2.1 Operational Principles of Artificial Neural Networks 11
2.1.1 Inference 11
2.1.2 Learning 13
2.2 Neural Network Based Machine Learning 16
2.2.1 Supervised Learning 17
2.2.2 Reinforcement Learning 20
2.2.3 Unsupervised Learning 22
2.2.4 Case Study: Action-Dependent Heuristic Dynamic Programming 23
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2.5 Deep Learning 41
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3 Artificial Neural Networks in Hardware 61
3.1 Overview 61
3.2 General-Purpose Processors 62
3.3 Digital Accelerators 63
3.3.1 A Digital ASIC Approach 63
3.4 Analog/Mixed-Signal Accelerators 82
3.5 Case Study: An Energy-Efficient Accelerator for Adaptive Dynamic Programming 94
4 Operational Principles and Learning in Spiking Neural Networks 119
4.1 Spiking Neural Networks 119
4.2 Learning in Shallow SNNs 124
4.3 Learning in Deep SNNs 146
5 Hardware Implementations of Spiking Neural Networks 173
5.1 The Need for Specialized Hardware 173
5.2 Digital SNNs 186
5.3 Analog/Mixed-Signal SNNs 210
6 Conclusions 247
A Appendix 257
Index 269