Contents
Jury composition vii
Preface ix
Acknowledgements xi
Author’s publications xv
Chapter I. Introduction 1
I.1. From machine learning to reservoir computing 1
I.1.1. Machine learning algorithms 1
I.1.2. Artificial neural networks 4
I.1.3. Reservoir computing 7
I.1.4. Benchmark tasks 12
I.2. Hardware implementations : opto-electronic delay systems 14
I.2.1. Time-multiplexing 15
I.2.2. Conceptual setup 16
I.2.3. Desynchronisation 18
I.2.4. Experimental setup 18
I.3. Field-Programmable Gate Arrays 23
I.3.1. History 23
I.3.2. Market and applications 26
I.3.3. Xilinx Virtex 6 : architecture and operation 27 I.3.4. Design flow and implementation tools 28 Chapter II. Online training of a photonic reservoir computer 33
II.1. Introduction 33
II.2. Equalisation of non-stationary channels 34 II.2.1. Influence of channel model parameters on equaliser performance 35
II.2.2. Slowly drifting channel 35
II.2.3. Switching channel 35
II.3. Online training 36
II.3.1. Gradient descent algorithm 37
II.4. Experimental setup 38
II.4.1. Input and readout 39
II.4.2. Experimental parameters 40
II.4.3. Experiment automation 40
II.5. FPGA design 41
II.6. Results 44
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II.6.1. Improved equalisation error rate 44
II.6.2. Simplified training algorithm 45
II.6.3. Equalisation of a slowly drifting channel 45 II.6.4. Equalisation of a switching channel 49 II.6.5. Influence of channel model parameters on equaliser performance 51
II.7. Challenges and solutions 51
II.8. Conclusion 53
Chapter III. Backpropagation with photonics 55
III.1. Introduction 55
III.2. Backpropagation through time 56
III.2.1. General idea and new notations 57
III.2.2. Setting up the problem 58
III.2.3. Output mask gradient 60
III.2.4. Input mask gradient 61
III.2.5. Multiple inputs/outputs 63
III.3. Experimental setup 63
III.3.1. Online multiplication using cascaded MZMs 65
III.3.2. Mask parametrisation 67
III.4. FPGA design 68
III.5. Results 70
III.5.1. Tasks 70
III.5.2. NARMA10 and VARDEL5 71
III.5.3. TIMIT 72
III.5.4. Gradient descent 74
III.5.5. Robustness 75
III.6. Challenges and solutions 76
III.7. Conclusion 77
Chapter IV. Photonic reservoir computer with output feedback 79
IV.1. Introduction 79
IV.2. Reservoir computing with output feedback 81
IV.3. Time series generation tasks 82
IV.3.1. Frequency generation 82
IV.3.2. Random pattern generation 83
IV.3.3. Mackey-Glass chaotic series prediction 83
IV.3.4. Lorenz chaotic series prediction 84
IV.4. Experimental setup 84
IV.5. FPGA design 86
IV.6. Numerical simulations 88
IV.7. Results 88
IV.7.1. Noisy reservoir 89
IV.7.2. Frequency generation 89
IV.7.3. Random pattern generation 91
IV.7.4. Mackey-Glass series prediction 96
IV.7.5. Lorenz series prediction 99
IV.8. Challenges and solutions 104
IV.9. Conclusion 105
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Chapter V. Towards online-trained analogue readout layer 109
V.1. Introduction 109
V.2. Methods 111
V.3. Proposed experimental setup 111
V.3.1. Analogue readout layer 111
V.3.2. FPGA board 113
V.4. Numerical simulations 113
V.5. Results 115
V.5.1. Linear readout: RC circuit 115
V.5.2. Nonlinear readout 118
V.6. Conclusion 118
Chapter VI. Real-time automated tissue characterisation for intravascular
OCT scans 121
VI.1. Introduction 121
VI.2. Feature extraction 127
VI.2.1. GLCM features 127
VI.2.2. Methods 130
VI.2.3. Operation principle 131
VI.2.4. FPGA design 132
VI.2.5. Results 134
VI.2.6. Perspectives 134
VI.3. Artificial neural network 135
VI.3.1. Network structure 135
VI.3.2. Methods 138
VI.3.3. Operation principle 140
VI.3.4. FPGA design 140
VI.3.5. Results 142
VI.4. Conclusion 142
Chapter VII. Conclusion and perspectives 145
Bibliography 151
