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Application of FPGA to real-time machine learning : hardware reservoir computers and software image processing

Author: Piotr Antonik
Publisher: Cham, Switzerland : Springer, 2018.
Series: Springer theses
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book lies at the interface of machine learning - a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail - and photonics - the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)1030899952
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Piotr Antonik
ISBN: 9783319910536 3319910531
OCLC Number: 1036987599
Notes: "Doctoral thesis accepted by the Université libre de Bruxelles, Brussels, Belgium."
Description: 1 online resource (xxii, 171 pages) : illustrations (some color)
Contents: Intro; Supervisor's Foreword; Abstract; Preface; Acknowledgements; Contents; 1 Introduction; 1.1 From Machine Learning to Reservoir Computing; 1.1.1 Machine Learning Algorithms; 1.1.2 Artificial Neural Networks; 1.1.3 Reservoir Computing; 1.1.4 Benchmark Tasks; 1.2 Hardware Implementations: Opto-Electronic Delay Systems; 1.2.1 Time-Multiplexing; 1.2.2 Conceptual Setup; 1.2.3 Desynchronisation; 1.2.4 Experimental Setup; 1.3 Field-Programmable Gate Arrays; 1.3.1 History; 1.3.2 Market and Applications; 1.3.3 Xilinx Virtex 6: Architecture and Operation; 1.3.4 Design Flow and Implementation Tools. 2.6.4 Equalisation of a Switching Channel2.6.5 Influence of Channel Model Parameters on Equaliser Performance; 2.7 Challenges and Solutions; 2.8 Conclusion; References; 3 Backpropagation with Photonics; 3.1 Introduction; 3.2 Backpropagation Through Time; 3.2.1 General Idea and New Notations; 3.2.2 Setting Up the Problem; 3.2.3 Output Mask Gradient; 3.2.4 Input Mask Gradient; 3.2.5 Multiple Inputs/Outputs; 3.3 Experimental Setup; 3.3.1 Online Multiplication Using Cascaded MZMs; 3.3.2 Mask Parametrisation; 3.4 FPGA Design; 3.5 Results; 3.5.1 Tasks; 3.5.2 NARMA10 and VARDEL5; 3.5.3 TIMIT. 3.5.4 Gradient Descent3.5.5 Robustness; 3.6 Challenges and Solutions; 3.7 Conclusion; References; 4 Photonic Reservoir Computer with Output Feedback; 4.1 Introduction; 4.2 Reservoir Computing with Output Feedback; 4.3 Time Series Generation Tasks; 4.3.1 Frequency Generation; 4.3.2 Random Pattern Generation; 4.3.3 Mackey-Glass Chaotic Series Prediction; 4.3.4 Lorenz Chaotic Series Prediction; 4.4 Experimental Setup; 4.5 FPGA Design; 4.6 Numerical Simulations; 4.7 Results; 4.7.1 Noisy Reservoir; 4.7.2 Frequency Generation; 4.7.3 Random Pattern Generation; 4.7.4 Mackey-Glass Series Prediction. 4.7.5 Lorenz Series Prediction4.8 Challenges and Solutions; 4.9 Conclusion; References; 5 Towards Online-Trained Analogue Readout Layer; 5.1 Introduction; 5.2 Methods; 5.3 Proposed Experimental Setup; 5.3.1 Analogue Readout Layer; 5.3.2 FPGA Board; 5.4 Numerical Simulations; 5.5 Results; 5.5.1 Linear Readout: RC Circuit; 5.5.2 Nonlinear Readout; 5.6 Conclusion; References; 6 Real-Time Automated Tissue Characterisation for Intravascular OCT Scans; 6.1 Introduction; 6.2 Feature Extraction; 6.2.1 GLCM Features; 6.2.2 Methods; 6.2.3 Operation Principle; 6.2.4 FPGA Design; 6.2.5 Results.
Series Title: Springer theses
Responsibility: Piotr Antonik.

Abstract:

This book lies at the interface of machine learning - a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail - and photonics - the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

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