Hardware Annealing In Analog Vlsi Neurocomputing
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Hardware Annealing In Analog Vlsi Neurocomputing
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Author : Bank W. Lee
language : en
Publisher: Springer
Release Date : 2012-12-06
Hardware Annealing In Analog Vlsi Neurocomputing written by Bank W. Lee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Rapid advances in neural sciences and VLSI design technologies have provided an excellent means to boost the computational capability and efficiency of data and signal processing tasks by several orders of magnitude. With massively parallel processing capabilities, artificial neural networks can be used to solve many engineering and scientific problems. Due to the optimized data communication structure for artificial intelligence applications, a neurocomputer is considered as the most promising sixth-generation computing machine. Typical applica tions of artificial neural networks include associative memory, pattern classification, early vision processing, speech recognition, image data compression, and intelligent robot control. VLSI neural circuits play an important role in exploring and exploiting the rich properties of artificial neural networks by using pro grammable synapses and gain-adjustable neurons. Basic building blocks of the analog VLSI neural networks consist of operational amplifiers as electronic neurons and synthesized resistors as electronic synapses. The synapse weight information can be stored in the dynamically refreshed capacitors for medium-term storage or in the floating-gate of an EEPROM cell for long-term storage. The feedback path in the amplifier can continuously change the output neuron operation from the unity-gain configuration to a high-gain configuration. The adjustability of the vol tage gain in the output neurons allows the implementation of hardware annealing in analog VLSI neural chips to find optimal solutions very efficiently. Both supervised learning and unsupervised learning can be implemented by using the programmable neural chips.
Hardware Annealing In Analog Vlsi Neurocomputing
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Author : Bank W. Lee
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Hardware Annealing In Analog Vlsi Neurocomputing written by Bank W. Lee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Rapid advances in neural sciences and VLSI design technologies have provided an excellent means to boost the computational capability and efficiency of data and signal processing tasks by several orders of magnitude. With massively parallel processing capabilities, artificial neural networks can be used to solve many engineering and scientific problems. Due to the optimized data communication structure for artificial intelligence applications, a neurocomputer is considered as the most promising sixth-generation computing machine. Typical applica tions of artificial neural networks include associative memory, pattern classification, early vision processing, speech recognition, image data compression, and intelligent robot control. VLSI neural circuits play an important role in exploring and exploiting the rich properties of artificial neural networks by using pro grammable synapses and gain-adjustable neurons. Basic building blocks of the analog VLSI neural networks consist of operational amplifiers as electronic neurons and synthesized resistors as electronic synapses. The synapse weight information can be stored in the dynamically refreshed capacitors for medium-term storage or in the floating-gate of an EEPROM cell for long-term storage. The feedback path in the amplifier can continuously change the output neuron operation from the unity-gain configuration to a high-gain configuration. The adjustability of the vol tage gain in the output neurons allows the implementation of hardware annealing in analog VLSI neural chips to find optimal solutions very efficiently. Both supervised learning and unsupervised learning can be implemented by using the programmable neural chips.
Cellular Neural Networks And Analog Vlsi
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Author : Leon Chua
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Cellular Neural Networks And Analog Vlsi written by Leon Chua and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Technology & Engineering categories.
Cellular Neural Networks and Analog VLSI brings together in one place important contributions and up-to-date research results in this fast moving area. Cellular Neural Networks and Analog VLSI serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Symbolic Analysis For Automated Design Of Analog Integrated Circuits
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Author : Georges Gielen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Symbolic Analysis For Automated Design Of Analog Integrated Circuits written by Georges Gielen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
It is a great honor to provide a few words of introduction for Dr. Georges Gielen's and Prof. Willy Sansen's book "Symbolic analysis for automated design of analog integrated circuits". The symbolic analysis method presented in this book represents a significant step forward in the area of analog circuit design. As demonstrated in this book, symbolic analysis opens up new possibilities for the development of computer-aided design (CAD) tools that can analyze an analog circuit topology and automatically size the components for a given set of specifications. Symbolic analysis even has the potential to improve the training of young analog circuit designers and to guide more experienced designers through second-order phenomena such as distortion. This book can also serve as an excellent reference for researchers in the analog circuit design area and creators of CAD tools, as it provides a comprehensive overview and comparison of various approaches for analog circuit design automation and an extensive bibliography. The world is essentially analog in nature, hence most electronic systems involve both analog and digital circuitry. As the number of transistors that can be integrated on a single integrated circuit (IC) substrate steadily increases over time, an ever increasing number of systems will be implemented with one, or a few, very complex ICs because of their lower production costs.
Neural Information Processing And Vlsi
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Author : Bing J. Sheu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Information Processing And Vlsi written by Bing J. Sheu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.
World Congress On Neural Networks
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Author : Paul Werbos
language : en
Publisher: Routledge
Release Date : 2021-09-09
World Congress On Neural Networks written by Paul Werbos and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-09 with Psychology categories.
Centered around 20 major topic areas of both theoretical and practical importance, the World Congress on Neural Networks provides its registrants -- from a diverse background encompassing industry, academia, and government -- with the latest research and applications in the neural network field.
High Level Vlsi Synthesis
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Author : Raul Camposano
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
High Level Vlsi Synthesis written by Raul Camposano and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
The time has come for high-level synthesis. When research into synthesizing hardware from abstract, program-like de scriptions started in the early 1970' s, there was no automated path from the register transfer design produced by high-level synthesis to a complete hardware imple mentation. As a result, it was very difficult to measure the effectiveness of high level synthesis methods; it was also hard to justify to users the need to automate architecture design when low-level design had to be completed manually. Today's more mature CAD techniques help close the gap between an automat ically synthesized design and a manufacturable design. Market pressures encour age designers to make use of any and all automated tools. Layout synthesis, logic synthesis, and specialized datapath generators make it feasible to quickly imple ment a register-transfer design in silicon,leaving designers more time to consider architectural improvements. As IC design becomes more automated, customers are increasing their demands; today's leading edge designers using logic synthesis systems are training themselves to be tomorrow's consumers of high-level synthe sis systems. The need for very fast turnaround, a competitive fabrication market WhlCh makes small-quantity ASIC manufacturing possible, and the ever growing co:n plexity of the systems being designed, all make higher-level design automaton inevitable.
Silicon On Insulator Technology
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Author : J.-P. Colinge
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Silicon On Insulator Technology written by J.-P. Colinge and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Technology & Engineering categories.
5. 2. Distinction between thick- and thin-film devices . . . . . . . . . . . . . . . . . . . . 109 5. 3. I-V Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5. 3. 1. Threshold voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 5. 3 . 2. Body effecL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 8 5. 3. 3. Short-channel effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5. 3. 4. Output characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 24 5. 4. Transconductance and mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 5. 4. 1 Transconductance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 5. 4. 2. Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 5. 5. Subthreshold slope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 5. 6. Impact ionization and high-field effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 9 5. 6. 1. Kink effecL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 39 5. 6. 2. Hot-electron degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 5. 7. Parasitic bipolar effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 5. 7. 1. Anomalous subthreshold slope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 45 5. 7. 2. Reduced drain breakdown voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 7 5. 8. Accumulation-mode p-channel MOSFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 9 CHAPTER 6 - Other SOl Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5 9 6. 1. Non-conventional devices adapted from bulk . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 6. 1. 1. COMFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 6. 1. 2. High-voltage lateral MOSFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 6 1 6. 1. 3. PIN photodiode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 6. 1. 4. JFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 6. 2. Novel SOl devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 6. 2. 1. Lubistor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 6. 2. 2. Bipolar-MOS device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 6. 2. 3. Double-gate MOSFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 69 6. 2. 4. Bipolar transistors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6. 2. 5. Optical modulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 74 CHAPTER 7 - The sm MOSFET Operating in a Harsh Environment. . . . . . . . 1 77 7. 1. Radiation environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 7 7. 1. 1. SEU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 7. 1. 2. Total dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 7. 1. 3. Dose-rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 8 4 7. 2. High-temperature operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 85 7. 2. 1. Leakage currents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Neural Networks And Speech Processing
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Author : David P. Morgan
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Networks And Speech Processing written by David P. Morgan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
We would like to take this opportunity to thank all of those individ uals who helped us assemble this text, including the people of Lockheed Sanders and Nestor, Inc., whose encouragement and support were greatly appreciated. In addition, we would like to thank the members of the Lab oratory for Engineering Man-Machine Systems (LEMS) and the Center for Neural Science at Brown University for their frequent and helpful discussions on a number of topics discussed in this text. Although we both attended Brown from 1983 to 1985, and had offices in the same building, it is surprising that we did not meet until 1988. We also wish to thank Kluwer Academic Publishers for their profes sionalism and patience, and the reviewers for their constructive criticism. Thanks to John McCarthy for performing the final proof, and to John Adcock, Chip Bachmann, Deborah Farrow, Nathan Intrator, Michael Perrone, Ed Real, Lance Riek and Paul Zemany for their comments and assistance. We would also like to thank Khrisna Nathan, our most unbi ased and critical reviewer, for his suggestions for improving the content and accuracy of this text. A special thanks goes to Steve Hoffman, who was instrumental in helping us perform the experiments described in Chapter 9.
Microwave Semiconductor Devices
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Author : Sigfrid Yngvesson
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Microwave Semiconductor Devices written by Sigfrid Yngvesson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
We have reached the double conclusion: that invention is choice, that this choice is imperatively governed by the sense of scientific beauty. Hadamard (1945), Princeton University Press, by permission. The great majority of all sources and amplifiers of microwave energy, and all devices for receiving or detecting microwaves, use a semiconductor active element. The development of microwave semiconductor devices, de scribed in this book, has proceeded from the simpler, two-terminal, devices such as GUNN or IMPATT devices, which originated in the 1960s, to the sophisticated monolithic circuit MESFET three-terminal active elements, of the 1980s and 1990s. The microwave field has experienced a renais sance in electrical engineering departments in the last few years, and much of this growth has been associated with microwave semiconductor devices. The University of Massachusetts has recently developed a well recognized program in microwave engineering. Much of the momentum for this pro gram has been provided by interaction with industrial companies, and the influx of a large number of industry-supported students. This program had a need for a course in microwave semiconductor devices, which covered the physical aspects, as well as the aspects of interest to the engineer who incorporates such devices in his designs. It was also felt that it would be im portant to introduce the most recently developed devices (HFETs, HBTs, and other advanced devices) as early as possible.