京都大学 / 0048 / 新制・課程博士 / 博士(地球環境学) / 甲第18704号 / 地環博第127号 / 新制||地環||26(附属図書館) / 31637 / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 梶井 克純, 教授 杉山 雅人, 准教授 清中 茂樹 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医科学) / 甲第21025号 / 医科博第86号 / 新制||医科||6(附属図書館) / 京都大学大学院医学研究科医科学専攻 / (主査)教授 斎藤 通紀, 教授 渡邊 直樹, 教授 岩井 一宏 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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This study aimed to understand the processes that govern free surface behaviour in depth-limited turbulent flows. Experimental data has shown that the turbulence properties at a point near the free surface relate directly to the properties of the free surface pattern. This would suggest a direct linkage between the free surface and the underlying turbulence field, but this cannot be true since the free surface pattern is strongly dynamic while the sub-surface turbulence field is relatively persistent. An oscillatory spatial correlation function was derived which explains the de-linkage, showing that the turbulence-generated surface pattern periodically inverts as it advects downstream. A model was developed, which shows that the observed free surfaces can be considered as an ensemble of overlapping but behaviourally independent oscillons. These are shown to influence a zone of fluid beneath the surface and invert at a frequency which is a function of the root-mean-square roughness height of the free surface. The spatial frequency of free surface oscillation relates strongly to the spatial frequency of turbulent structures, suggesting that the oscillon motion may form the trigger for near-bed bursting events. Given these relationships, it is proposed that measurement of the free surface behaviour may allow remote measurement of flow conditions. An acoustic wave probe was developed, which is able to remotely recover the key features of the water surface pattern. An array of such probes is proposed for the accurate measurement of temporal and spatial properties of turbulent free surfaces and hence the underlying bulk flow conditions.

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Von Neumann Bottleneck, which refers to the limited throughput between the CPU and memory, has already become the major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain increasing attention as compact and energy-efficient computing platforms. Spike based-neuromorphic computing systems require high performance and low power neural encoder and decoder to emulate the spiking behavior of neurons. These two spike-analog signals converting interface determine the whole spiking neuromorphic computing system's performance, especially the highest performance. Many state-of-the-art neuromorphic systems typically operate in the frequency range between 〖10〗^0KHz and 〖10〗^2KHz due to the limitation of encoding/decoding speed. In this dissertation, all these popular encoding and decoding schemes, i.e. rate encoding, latency encoding, ISI encoding, together with related hardware implementations have been discussed and analyzed. The contributions included in this dissertation can be classified into three main parts: neuron improvement, three kinds of ISI encoder design, two types of ISI decoder design. Two-path leakage LIF neuron has been fabricated and modular design methodology is invented. Three kinds of ISI encoding schemes including parallel signal encoding, full signal iteration encoding, and partial signal encoding are discussed. The first two types ISI encoders have been fabricated successfully and the last ISI encoder will be taped out by the end of 2019. Two types of ISI decoders adopted different techniques which are sample-and-hold based mixed-signal design and spike-timing-dependent-plasticity (STDP) based analog design respectively. Both these two ISI encoders have been evaluated through post-layout simulations successfully. The STDP based ISI encoder will be taped out by the end of 2019. A test bench based on correlation inspection has been built to evaluate the information recovery capability of the proposed spiking processing link. / Doctor of Philosophy / Neuromorphic computing is a kind of specific electronic system that could mimic biological bodies’ behavior. In most cases, neuromorphic computing system is built with analog circuits which have benefits in power efficient and low thermal radiation. Among neuromorphic computing system, one of the most important components is the signal processing interface, i.e. encoder/decoder. To increase the whole system’s performance, novel encoders and decoders have been proposed in this dissertation. In this dissertation, three kinds of temporal encoders, one rate encoder, one latency encoder, one temporal decoder, and one general spike decoder have been proposed. These designs could be combined together to build high efficient spike-based data link which guarantee the processing performance of whole neuromorphic computing system.

In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We develop a novel input and output processing engine for our network and demonstrate the spatio-temporal information processing capability. We demonstrate an accuracy of a 100% with our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations. / M.S. / In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. Artificial Neural Networks (ANNs) are models that mimic biological neurons where artificial neurons or neurodes are connected together via synapses, similar to the nervous system in the human body. here are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of power and energy when carrying out data intensive applications by allowing spatio-temporal information processing capability. On the other hand, emerging non-volatile memory (eNVM) technology is key to emulate neurons and synapses for in-memory computations for neuromorphic hardware. A particular eNVM technology, memristors, have received wide attention due to their scalability, compatibility with CMOS technology and low power consumption properties. In this work we develop a spiking neural network by incorporating an inter-spike interval encoding scheme to convert the incoming input signal to spikes and use a memristive crossbar to carry out in-memory computing operations. We demonstrate the accuracy of our design through a small-scale hardware simulation for digit recognition and demonstrate an accuracy of 87% in software through MNIST simulations.

As Moore's law nears its limit, a new form of signal processing is needed. Neuromorphic computing has used inspiration from biology to produce a new form of signal processing by mimicking biological neural networks using electrical components. Neuromorphic computing requires less signal preprocessing than digital systems since it can encode signals directly using analog temporal encoders from Spiking Neural Networks (SNNs). These encoders receive an analog signal as an input and generate a spike or spike trains as their output. The proposed temporal encoders use latency and Inter-Spike Interval (ISI) encoding and are expected to produce a highly sensitive hardware implementation of time encoding to preprocess signals for dynamic neural processors. Two ISI and two latency encoders were designed using Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF) neurons and optimized to produce low area designs. The IF and LIF neurons were designed using the Global Foundries 180nm CMOS process and achieved an area of 186µm2 and 182µm2, respectively. All four encoders have a sampling frequency of 50kHz. The latency encoders achieved an average energy consumption per spike of 277nJ and 316pJ for the IF-based and LIF-based latency encoders, respectively. The ISI encoders achieved an average energy consumption per spike of 1.07uJ and 901nJ for the IF-based and LIF-based ISI encoders, respectively. Power consumption is proportional to the number of neurons employed in the encoder and the potential to reduce power consumption through layout-level simulations is presented. The LIF neuron is able to use a smaller membrane capacitance to achieve similar operability as the IF neuron and consumes less area despite having more components. This demonstrates that capacitor sizes are the main limitations of a small size in spiking neurons for SNNs. An overview of the design and layout process of the two presented neurons is discussed with tips for overcoming problems encountered. The proposed designs can result in a fast neuromorphic process by employing a frequency higher than 10kHz and by providing a hardware implementation that is efficient in multiple sectors like machine learning, medical implementations, or security systems since hardware is safer from hacks. / Master of Science / As Moore's law nears its limit, a new form of signal processing is needed. Moore's law anticipated that transistor sizes will decrease exponentially as the years pass but CMOS technology is reaching physical limitations which could mean an end to Moore's prediction. Neuromorphic computing has used inspiration from biology to produce a new form of signal processing by mimicking biological neural networks using electrical components. Biological neural networks communicate through interconnected neurons that transmit signals through synapses. Neuromorphic computing uses a subdivision of Artificial Neural Networks (ANNs) called Spiking Neural Networks (SNNs) to encode input signals into voltage spikes to mimic biological neurons. Neuromorphic computing reduces the preprocessing step needed to process data in the digital domain since it can encode signals directly using analog temporal encoders from SNNs. These encoders receive an analog signal as an input and generate a spike or spike trains as their output. The proposed temporal encoders use latency and Inter-Spike Interval (ISI) encoding and are expected to produce a highly sensitive hardware implementation of time encoding to preprocess signals for dynamic neural processors. Two ISI and two latency encoders were designed using Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF) neurons and optimized to produce low area designs. All four encoders have a sampling frequency of 50kHz. The latency encoders achieved an average energy consumption per spike of 277nJ and 316pJ for the IF-based and LIF-based latency encoders, respectively. The ISI encoders achieved an average energy consumption per spike of 1.07uJ and 901nJ for the IF-based and LIF-based ISI encoders, respectively. Power consumption is proportional to the number of neurons employed in the encoder and the potential to reduce power consumption through layout-level simulations is presented. The LIF neuron is able to use a smaller membrane capacitance to achieve similar operability which consumes less area despite having more components than the IF neuron. This demonstrates that capacitor sizes are the main limitations of small size in neurons for spiking neural networks. An overview of the design and layout process of the two presented neurons is discussed with tips for overcoming problems encountered. The proposed designs can result in a fast neuromorphic process by employing a frequency higher than 10kHz and by providing a hardware implementation that is efficient in multiple sectors like machine learning, medical implementations, or security systems since hardware is safer from hacks.

L'étude décrite dans cette thèse porte sur l'injection ponctuelle d'une solution saline au sein d'une cellule dite de Hele-Shaw, afin de caractériser le comportement dispersif d'un polluant en milieu poreux. L'approche expérimentale employée est basée sur l'implémentation originale d'un dispositif de Fluorescence Induite par Laser (LIF) dans la cellule. La mise en place d'un protocole de mesure efficace permet de mener une analyse quantitative des résultats expérimentaux. En outre, en appliquant la méthode des moments, il est possible de caractériser avec précision le comportement dispersif de la zone de mélange de la solution injectée. Parallèlement aux expériences, à l'aide du code numérique FRIPE, les injections ont été simulées numériquement. L'analyse quantitative a été appliquée à ces dernières. Une comparaison poussée des résultats expérimentaux et numériques a donc été effectuée, du point de vue qualitatif mais aussi sur l'expression de la dispersion du panache de la zone de mélange de la solution / The study described in this thesis is about punctual injection of a saline solution inside a "Hele-Shaw cell" in order to characterize the dispersive behavior of a pollutant in porous media. The chosen experimental approach is based on the setup of an original Laser Induced Fluorescence (LIF) in the Hele-Shaw cell. The setting of the experimental apparatus allows quantitative data reduction of the experimental results. Moreover the "Moments Method" studied precisely the solution mixing dispersive behavior. Using the numerical code FRIPE the same injections have been simulated. The same quantitative data reductions have been applied to the numerical results. This led to an extensive comparison of the numerical and the experimental results, qualitatively but also of the dispersion in the mixing area of the injected solution