Brainware : Bio-Inspired Architecture and Its Hardware Implementation.

By: Miki, TsutomuSeries: Fuzzy Logic Systems Institute (Flsi) Soft Computing SerPublisher: Singapore : World Scientific Publishing Co Pte Ltd, 2001Copyright date: ©2001Description: 1 online resource (245 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9789812810250Subject(s): Computer architecture.;Neural computersGenre/Form: Electronic books. Additional physical formats: Print version:: Brainware : Bio-Inspired Architecture and Its Hardware ImplementationDDC classification: 006.3 LOC classification: QA76.87.B72 2001Online resources: Click to View
Contents:
Intro -- Contents -- Series Editor's Preface -- Volume Editor's Preface -- Chapter 1 Neuron MOS Transistor: The Concept and Its Application -- 1.1 Introduction -- 1.2 Bio Processing vs. Electronic Processing -- 1.3 Neuron MOS and Elemental Logic Gate -- 1.4 Application to Binary Logic Gate -- 1.5 Example of Hardware Algorithm: Center of Mass Circuit -- 1.6 Right-Brain Computing Model and Association Processor -- 1.7 Applications of Association Processor Architecture -- 1.8 Conclusions -- References -- Chapter 2 Adaptive Learning Neuron Integrated Circuits Using Ferroelectric-Gate FETs -- 2.1 Introduction -- 2.2 Operation Principles of Adaptive-Learning Neuron Circuits -- 2.3 Neuron Integrated Circuits Composed of MFSFETs and CUJT Oscillation Circuits -- 2.4 Neuron Circuit Using CMOS Schmitt-Trigger Oscillator -- 2.5 Conclusions -- References -- Chapter 3 An Analog-digital Merged Circuit Architecture Using PWM Techniques for Bio-Inspired Nonlinear Dynamical Systems -- 3.1 Introduction -- 3.2 A New VLSI Implementation Approach Using PWM Signals -- 3.3 A Neural Circuit Using PWM Signals -- 3.4 Arbitrary Nonlinear Transformation Using PWM Signals -- 3.5 Conclusion -- References -- Chapter 4 Application-Driven Design of Bio-Inspired Low-Power Vision Circuits & Systems -- 4.1 Introduction -- 4.2 Methodology for application-specific design of vision circuits and systems -- 4.3 Design examples of integrated low-power vision systems -- 4.4 Conclusions and Future Work -- References -- Chapter 5 Motion Detection with Bio-Inspired Analog MOS Circuits -- 5.1 Introduction -- 5.2 Correlation Neural Networks for the Motion Detection -- 5.3 Velocity Sensing Circuits and Networks for the Correlation Model -- 5.4 Simulation Results -- 5.5 VSC Networks and Computational Algorithm for Optical Flows -- 5.6 Summary and Discussion -- 5.7 Acknowledgement -- References.
Chapter 6 vMOS Cellular-Automaton Circuit for Picture Processing -- 6.1 Introduction -- 6.2 The Cellular Automaton for Morphological Picture Processing -- 6.3 Construction of Cell Circuits using vMOS FETs -- 6.4 Properties of Cell Circuits using vMOS FET -- 6.5 Image Thinning and Shrinking by a Cellular Automaton -- 6.6 Hardware Embodiment of the Cellular Automaton for Thinning and Shrinking -- 6.7 Simulation of Operational Characteristics of a vMOS Cellular Automaton -- 6.8 Constructing a vMOS Circuit that has Low Power Dissipation -- 6.9 Conclusions -- Appendix A Simulation Analyses of Low Power vMOS circuits -- References -- Chapter 7 Semiconductor Chaos-Generating Elements of Simple Structure and Their Integration -- 7.1 Introduction -- 7.2 Capacitor-Transistor Pair -- 7.3 Return-Map Unit -- 7.4 CMOS Chaos Multivibrator -- 7.4 Chaos Generation from the Pipelined A-D Converter -- 7.6 Conclusions -- References -- Chapter 8 Computation in Single Neuron with Dendritic Trees -- 8.1 Introduction -- 8.2 Computational Consequence of Passive Dendrites -- 8.3 Functional Significance of Active Dendrites -- 8.4 Discussion and Conclusion -- Appendix A Compartmental Model of Pyramidal Neuron -- References -- About the Authors -- Keyword Index.
Summary: The human brain, the ultimate intelligent processor, can handle ambiguous and uncertain information adequately. The implementation of such a human-brain architecture and function is called "brainware". Brainware is a candidate for the new tool that will realize a human-friendly computer society. As one of the LSI implementations of brainware, a "bio-inspired" hardware system is discussed in this book. Consisting of eight enriched versions of papers selected from IIZUKA '98, this volume provides wide coverage, from neuronal function devices to vision systems, chaotic systems, and also an effective design methodology of hierarchical large-scale neural systems inspired by neuroscience. It can serve as a reference for graduate students and researchers working in the field of brainware. It is also a source of inspiration for research towards the realization of a silicon brain. Contents: Neuron MOS Transistor: The Concept and Its Application (T Shibata); Adaptive Learning Neuron Integrated Circuits Using Ferroelectric-Gate FETs (S-M Yoon et al.); An Analog-Digital Merged Circuit Architecture Using PWM Techniques for Bio-Inspired Nonlinear Dynamical Systems (T Morie et al.); Application-Driven Design of Bio-Inspired Low-Power Vision Circuits and Systems (A König et al.); Motion Detection with Bio-Inspired Analog MOS Circuits (H Yonezu et al.); ν MOS Cellular-Automaton Circuit for Picture Processing (M Ikebe & Y Amemiya); Semiconductor Chaos-Generating Elements of Simple Structure and Their Integration (K Hoh et al.); Computation in Single Neuron with Dendritic Trees (N Katayama et al.). Readership: Graduate students, researchers and industrialists in artificial intelligence, neural networks, machine perception, computer vision, pattern/handwriting recognition, image analysis and biocomputing.
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Intro -- Contents -- Series Editor's Preface -- Volume Editor's Preface -- Chapter 1 Neuron MOS Transistor: The Concept and Its Application -- 1.1 Introduction -- 1.2 Bio Processing vs. Electronic Processing -- 1.3 Neuron MOS and Elemental Logic Gate -- 1.4 Application to Binary Logic Gate -- 1.5 Example of Hardware Algorithm: Center of Mass Circuit -- 1.6 Right-Brain Computing Model and Association Processor -- 1.7 Applications of Association Processor Architecture -- 1.8 Conclusions -- References -- Chapter 2 Adaptive Learning Neuron Integrated Circuits Using Ferroelectric-Gate FETs -- 2.1 Introduction -- 2.2 Operation Principles of Adaptive-Learning Neuron Circuits -- 2.3 Neuron Integrated Circuits Composed of MFSFETs and CUJT Oscillation Circuits -- 2.4 Neuron Circuit Using CMOS Schmitt-Trigger Oscillator -- 2.5 Conclusions -- References -- Chapter 3 An Analog-digital Merged Circuit Architecture Using PWM Techniques for Bio-Inspired Nonlinear Dynamical Systems -- 3.1 Introduction -- 3.2 A New VLSI Implementation Approach Using PWM Signals -- 3.3 A Neural Circuit Using PWM Signals -- 3.4 Arbitrary Nonlinear Transformation Using PWM Signals -- 3.5 Conclusion -- References -- Chapter 4 Application-Driven Design of Bio-Inspired Low-Power Vision Circuits & Systems -- 4.1 Introduction -- 4.2 Methodology for application-specific design of vision circuits and systems -- 4.3 Design examples of integrated low-power vision systems -- 4.4 Conclusions and Future Work -- References -- Chapter 5 Motion Detection with Bio-Inspired Analog MOS Circuits -- 5.1 Introduction -- 5.2 Correlation Neural Networks for the Motion Detection -- 5.3 Velocity Sensing Circuits and Networks for the Correlation Model -- 5.4 Simulation Results -- 5.5 VSC Networks and Computational Algorithm for Optical Flows -- 5.6 Summary and Discussion -- 5.7 Acknowledgement -- References.

Chapter 6 vMOS Cellular-Automaton Circuit for Picture Processing -- 6.1 Introduction -- 6.2 The Cellular Automaton for Morphological Picture Processing -- 6.3 Construction of Cell Circuits using vMOS FETs -- 6.4 Properties of Cell Circuits using vMOS FET -- 6.5 Image Thinning and Shrinking by a Cellular Automaton -- 6.6 Hardware Embodiment of the Cellular Automaton for Thinning and Shrinking -- 6.7 Simulation of Operational Characteristics of a vMOS Cellular Automaton -- 6.8 Constructing a vMOS Circuit that has Low Power Dissipation -- 6.9 Conclusions -- Appendix A Simulation Analyses of Low Power vMOS circuits -- References -- Chapter 7 Semiconductor Chaos-Generating Elements of Simple Structure and Their Integration -- 7.1 Introduction -- 7.2 Capacitor-Transistor Pair -- 7.3 Return-Map Unit -- 7.4 CMOS Chaos Multivibrator -- 7.4 Chaos Generation from the Pipelined A-D Converter -- 7.6 Conclusions -- References -- Chapter 8 Computation in Single Neuron with Dendritic Trees -- 8.1 Introduction -- 8.2 Computational Consequence of Passive Dendrites -- 8.3 Functional Significance of Active Dendrites -- 8.4 Discussion and Conclusion -- Appendix A Compartmental Model of Pyramidal Neuron -- References -- About the Authors -- Keyword Index.

The human brain, the ultimate intelligent processor, can handle ambiguous and uncertain information adequately. The implementation of such a human-brain architecture and function is called "brainware". Brainware is a candidate for the new tool that will realize a human-friendly computer society. As one of the LSI implementations of brainware, a "bio-inspired" hardware system is discussed in this book. Consisting of eight enriched versions of papers selected from IIZUKA '98, this volume provides wide coverage, from neuronal function devices to vision systems, chaotic systems, and also an effective design methodology of hierarchical large-scale neural systems inspired by neuroscience. It can serve as a reference for graduate students and researchers working in the field of brainware. It is also a source of inspiration for research towards the realization of a silicon brain. Contents: Neuron MOS Transistor: The Concept and Its Application (T Shibata); Adaptive Learning Neuron Integrated Circuits Using Ferroelectric-Gate FETs (S-M Yoon et al.); An Analog-Digital Merged Circuit Architecture Using PWM Techniques for Bio-Inspired Nonlinear Dynamical Systems (T Morie et al.); Application-Driven Design of Bio-Inspired Low-Power Vision Circuits and Systems (A König et al.); Motion Detection with Bio-Inspired Analog MOS Circuits (H Yonezu et al.); ν MOS Cellular-Automaton Circuit for Picture Processing (M Ikebe & Y Amemiya); Semiconductor Chaos-Generating Elements of Simple Structure and Their Integration (K Hoh et al.); Computation in Single Neuron with Dendritic Trees (N Katayama et al.). Readership: Graduate students, researchers and industrialists in artificial intelligence, neural networks, machine perception, computer vision, pattern/handwriting recognition, image analysis and biocomputing.

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