Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design (Hardcover)

Chapter 1 Overview 1

1.1 History of Neural Networks 1

1.2 Neural Networks in Software 2

1.2.1 ANN 2

1.2.2 SNN 3

1.3 Need for Neuromorphic Hardware 4

1.4 Objectives and Outlines of the Book 6

Chapter 2 Fundamentals and Learning of Artificial Neural Networks 1

2.1 Operational Principles of Artificial Neural Networks 1

2.1.1 Inference 1

2.1.2 Learning 4

2.2 Neural Network Based Machine Learning 8

2.2.1 Supervised Learning 8

2.2.2 Reinforcement Learning 11

2.2.3 Unsupervised Learning 14

2.2.4 Case Study: Action-Dependent Heuristic Dynamic Programming 16

2.3 Network Topologies 25

2.3.1 Fully-Connected Neural Networks 25

2.3.2 Convolutional Neural Networks 28

2.3.3 Recurrent Neural Networks 31

2.4 Dataset and Benchmarks 34

2.5 Deep Learning 38

2.5.1 Pre-Deep-Learning Era 38

2.5.2 The Rise of Deep Learning 38

2.5.3 Deep Learning Techniques 39

2.5.4 Deep Neural Network Examples 48

Chapter 3 Artificial Neural Networks in Hardware 1

3.1 Overview 1

3.2 General-Purpose Processors 2

3.3 Digital Accelerators 3

3.3.1 A Digital ASIC Approach 3

3.3.2 FPGA-Based Accelerators 24

3.4 Analog/Mixed-Signal Accelerators 26

3.4.1 Neural Networks in Conventional Integrated Technology 27

3.4.2 Neural Network Based on Emerging Non-Volatile Memory 34

3.4.3 Optical Accelerator 40

3.5 Case Study: An Energy-Efficient Accelerator for Adaptive Dynamic Programming 41

3.5.1 Hardware Architecture 43

3.5.2 Design Examples 50

Chapter 4 Operational Principles and Learning in SNNs 1

4.1 Spiking Neural Networks 1

4.1.1 Popular Spiking Neuron Models 1

4.1.2 Information Encoding 4

4.1.3 Spiking Neuron vs. Non-Spiking Neuron 5

4.2 Learning in Shallow SNNs 7

4.2.1 ReSuMe 8

4.2.2 Tempotron 9

4.2.3 Spike-Timing-Dependent Plasticity 11

4.2.4 Learning through Modulating Weight-Dependent STDP in Two-Layer Neural Networks 14

4.3 Learning in Deep SNNs 34

4.3.1 SpikeProp 34

4.3.2 Stack of Shallow Networks 35

4.3.3 Conversion from ANNs 37

4.3.4 Recent Advances in Backpropagation for Deep SNNs 38

4.3.5 Learning through Modulating Weight-Dependent STDP Multi-Layer Neural Networks 39

Chapter 5 Hardware Implementations of Spiking Neural Networks 1

5.1 The Need for Specialized Hardware 1

5.1.1 Address-Event Representation 1

5.1.2 Event-Driven Computation 2

5.1.3 Inference with A Progressive Precision 4

5.1.4 Hardware Considerations for Implementing the Weight-Dependent STDP Learning Rule 10

5.2 Digital SNNs 15

5.2.1 Large-Scale SNN ASICs 15

5.2.2 Small/Moderate-Scale Digital SNNs 23

5.2.3 Hardware-Friendly Reinforcement Learning in SNNs 26

5.2.4 Hardware-Friendly Supervised Learning in Multi-Layer SNNs 31

5.3 Analog/Mixed-Signal SNNs 43

5.3.1 Basic Building Blocks 43

5.3.2 Large-Scale Analog/Mixed-Signal CMOS SNNs 47

5.3.3 Other Analog/Mixed-Signal CMOS SNN ASICs 49

5.3.4 SNNs Based on Emerging Nanotechnologies 50

5.3.5 Case Study: Memristor Crossbar-based Learning in SNNs 55

Chapter 6 Conclusions 1

6.1 Outlooks 1

6.1.1 Brain-Inspired Computing 1

6.1.2 Emerging nanotechnologies 3

6.1.3 Reliable Computing with Neuromorphic Systems 4

6.1.4 Blending of ANNs and SNNs 6

6.2 Conclusions 7

Appendix