組合せ最適化問題のための測定フィードバック型コヒーレント・イジングマシンの実現と評価

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 合原 一幸, 東京大学教授 岩田 覚, 東京大学准教授 平田 祥人, 東京大学准教授 大西 立顕, 東京大学准教授 鈴木 大慈University of Tokyo(東京大学

Stochastic Memory Devices for Security and Computing

With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware-based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large-area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive-switching memory (RRAM), phase-change memory (PCM), and spin-transfer torque magnetic memory (STT-MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed

Locomotor patterns and persistent activity in self-organizing neural models

The thesis investigates principles of self-organization that may account for the observed structure and behaviour of neural networks that generate locomotor behaviour and complex spatiotemporal patterns such as spiral waves, metastable states and persistent activity. This relates to the general neuroscience problem of finding the correspondence between the structure of neural networks and their function. This question is both extremely important and difficult to answer because the structure of a neural network defines a specific type of neural dynamics which underpins some function of the neural system and also influences the structure and parameters of the network including connection strengths. This loop of influences results in a stable and reliable neural dynamics that realises a neural function. In order to study the relationship between neural network structure and spatiotemporal dynamics, several computational models of plastic neural networks with different architectures are developed. Plasticity includes both modification of synaptic connection strengths and adaptation of neuronal thresholds. This approach is based on a consideration of general modelling concepts and focuses on a relatively simple neural network which is still complex enough to generate a broad spectrum of spatio-temporal patterns of neural activity such as spiral waves, persistent activity, metastability and phase transitions. Having considered the dynamics of networks with fixed architectures, we go on to consider the question of how a neural circuit which realizes some particular function establishes its architecture of connections. The approach adopted here is to model the developmental process which results in a particular neural network structure which is relevant to some particular functionality; specifically we develop a biologically realistic model of the tadpole spinal cord. This model describes the self-organized process through which the anatomical structure of the full spinal cord of the tadpole develops. Electrophysiological modelling shows that this architecture can generate electrical activity corresponding to the experimentally observed swimming behaviour

Center for Space Microelectronics Technology 1988-1989 technical report

The 1988 to 1989 Technical Report of the JPL Center for Space Microelectronics Technology summarizes the technical accomplishments, publications, presentations, and patents of the center. Listed are 321 publications, 282 presentations, and 140 new technology reports and patents

Advancing the analysis of architectural fabric structures, neural networks and uncertainty

PhD ThesisIn current practice a plane stress framework comprising elastic moduli and Poisson’s ratios is most commonly used to represent the mechanical properties of architectural fabrics. This is often done to enable structural analysis utilising commercially available, non-specialist, finite element packages. Plane stress material models endeavour to fit a flat plane to the highly non-linear stress strain response surface of architectural fabric. Neural networks have been identified as a possible alternative to plane stress material models. Through a process of training they are capable of capturing the relationship between experimental input and output data. With the addition of historical inputs and internal variables it has been demonstrated that neural network models are capable of representing complex history dependant behaviour. The resulting neural network architectural fabric material models have been implemented within custom large strain finite element code. The finite element code, capable of using either a neural network or plane stress material model, utilises a dynamic relaxation solution algorithm and includes geodesic string control for soap film form-finding. Analytical FORM reliability analysis using implied stiffness matrices' derived from the equations of the neural network model has also been investigated

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included