Coarse-grained fabrics are comprised of multi-bit configurable logic blocks and configurable interconnect. This work is focused on area and energy optimization techniques for coarse-grained reconfigurable fabric architectures. In this work, a variety of design techniques have been explored to improve the utilization of computational resources and increase energy savings. This includes splitting, folding, multi-level vertical interconnect. In addition to this, I have also studied fully connected homogeneous and heterogeneous architectures, and 3D architecture. I have also examined some of the hybrid strategies of computation unit’s arrangements. In order to perform energy and area analysis, I selected a set of signal and image processing benchmarks from MediaBench suite. I implemented various fabric architectures on 90nm ASIC process from Synopsys. Results show area improvement with energy savings as compared to baseline architecture.

Medium chain fatty acids (MCFAs) are essential molecules for a wide range of pharmaceutical, biotechnological, and industrial applications. These are naturally occurring saturated or unsaturated fatty acids containing 6-12 carbons with complex and pH sensitive aggregation. Medium chain fatty acids such as capric acid (C10) or lauric acid (C12) have additionally been shown to exhibit antibacterial activity. A number of studies have observed the aggregation behavior of long chain fatty acid using the titration curves by molecular dynamic (MD) simulations.  In this study, we performed constant-pH coarse-grained MD simulations to determine pKa values and titration behavior using a two-states model for C10 and C12. In the simulations, pH was varied between 2 to 8 and pKa values were determined using the Hill equation. The pKa for C10 (capric acid) was found to be 4.8 and for C12 (lauric acid) 5.4, in good agreement with the literature values (4.9 and 5.3, respectively).

Diese Dissertation befasst sich mit dem Verhalten eines generischen semiflexi- blen Polymermodells. Insbesondere untersucht es den Einfluss von Steifigkeit auf die unterschiedlichen thermodynamisch stabilen Konformationen. Es wird erläutert wie durch die Steifigkeit des Polymers verschiedene struk- turierte Phasen induziert werden. Insbesondere wird dabei auf die sta- bilen verknoteten Phasen eingegangen. Der zweite Teil der Dissertation beschäftigt sich dann mit dem Einfluss einer kugelförmigen Einsperrung auf das Phasendiagramm des selben Polymermodells. Es wird gezeigt wie in Abhängigkeit der Ordnung des Phasenüberganges die Einsperrung entweder zu einem stabilisierenden oder destabilisierenden Effekt führt. Im dritten Teil der Dissertation werden dann die komplexen Monte-Carlo Simulationen erläutert die für die Simulation der physikalischen Systeme genutzt wurde. Diese Algorithmen wurden in ein Framework integriert, so dass diese wieder verwendet werden können.

<div>Organic mixed ion-electron conducting (OMIEC) polymers are capable of transporting both electrons and ions. This unique functionality underpins many emerging applications, including biosensors, electrochemical transistors, and batteries. The fundamental operating principles and structure-function relationships of OMIECs are still being investigated. Computational tools such as coarse-grained molecular dynamics (CGMD), which use simpler representations than in atomistic modeling, are ideal to study OMIECs, as they can explore the slow dynamics and large length scale features of polymers. Nevertheless, methods development is still required for CGMD simulations to accurately describe OMIECs.</div><div><br></div><div>In this thesis, two CGMD simulation approaches have been adopted. One is a so-called "top-down" approach to develop a generic model of OMIECs. Top-down models are phenomenological but capable of exploring a broad space of materials variables, including backbone anisotropy, persistence length, side-chain density, and hydrophilicity. This newly developed model was used to interrogate the effect of side-chain polarity and patterning on OMIEC physics. These studies reproduce experimentally observed polymer swelling while for the first time clarifying several molecular factors affecting charge transport, including the role of trap sites, polaron delocalization, electrolyte percolation, and suggesting side-chain patterning as a potential tool to improve OMIEC performance.</div><div><br></div><div>The second strategy pursued in this thesis is bottom-up CGMD modeling of specific atomistic systems. The bottom-up approach enables CGMD simulations to be quantitatively related to specific materials; yet, the sources of error and methods for addressing them have yet to be systematically established. To address this gap, we have studied the effect of the CG mapping operator, an important CG variable, on the fidelity of atomistic and CGMD simulations. A major observation from this study is that prevailing CGMD methods are underdetermined with respect to atomistic training data. In a separate study, we have proposed a hybrid machine-learning and physics-based CGMD framework that utilizes information from multiple sources and improves on the accuracy of ML-only bottom-up CGMD approaches. </div>

京都大学 / 新制・課程博士 / 博士(理学) / 甲第23405号 / 理博第4740号 / 新制||理||1679(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 高田 彰二, 教授 川口 真也, 准教授 立川 正志 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

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京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18117号 / 理博第3995号 / 新制||理||1576(附属図書館) / 30975 / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 高田 彰二, 教授 大野 睦人, 准教授 土井 知子 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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