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The Resource Application of FPGA to real-time machine learning : hardware reservoir computers and software image processing, Piotr Antonik, (electronic book)

Application of FPGA to real-time machine learning : hardware reservoir computers and software image processing, Piotr Antonik, (electronic book)

Label
Application of FPGA to real-time machine learning : hardware reservoir computers and software image processing
Title
Application of FPGA to real-time machine learning
Title remainder
hardware reservoir computers and software image processing
Statement of responsibility
Piotr Antonik
Creator
Author
Subject
Language
eng
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 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
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorName
Antonik, Piotr
Dewey number
006.3/1
Illustrations
illustrations
Index
no index present
LC call number
Q325.5
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
Springer theses,
http://library.link/vocab/subjectName
  • Machine learning
  • Field programmable gate arrays
Label
Application of FPGA to real-time machine learning : hardware reservoir computers and software image processing, Piotr Antonik, (electronic book)
Instantiates
Publication
Note
"Doctoral thesis accepted by the Université libre de Bruxelles, Brussels, Belgium."
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Extent
1 online resource (xxii, 171 pages)
Form of item
online
Isbn
9783319910536
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-91053-6
Other physical details
illustrations (some color).
System control number
  • on1036987599
  • (OCoLC)1036987599
Label
Application of FPGA to real-time machine learning : hardware reservoir computers and software image processing, Piotr Antonik, (electronic book)
Publication
Note
"Doctoral thesis accepted by the Université libre de Bruxelles, Brussels, Belgium."
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Extent
1 online resource (xxii, 171 pages)
Form of item
online
Isbn
9783319910536
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-91053-6
Other physical details
illustrations (some color).
System control number
  • on1036987599
  • (OCoLC)1036987599

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