Understanding and Estimating Uncertainties and Risks in Architecture Design: from Analytical Analysis to Detailed Simulation

Computer architecture is always changing. Now, more than ever, we see deeper vertical integration with domain-specific software, faster emergence of paradigm shifting computing devices and memory technologies, and unprecedented security and privacy vulnerabilities. These changes all present opportunities for innovations in architecture design, and along with them, uncertainties we have to deal with carefully. An uncertainty-aware approach is essential when we design computer architectures and systems for the future, as variations in technology alone has been demonstrated to have the potential of eliminating the performance gain of an entire CMOS generation. To navigate the new dark waters exposed by changes in fabrication, design constraints, and programming models, a clear understanding and rigorous quantification of uncertainties in architecture designs, as well as the risks that come along, is a critical first step.To achieve such a goal in a tremendous space of designs, from the smallest embedded system to the largest warehouse-scale computing infrastructure, from the most well-characterized CMOS technology node to novel devices at the edge of our understanding, we need new systematic supports across the design stack from high level analytical analysis when evaluating design decisions in the early stage to cycle-accurate detailed simulations when projecting actual system performance.This thesis establishes a route to build a new uncertainty and risk aware architecture design process. We first demonstrate how even a very high level definition and understanding of such concepts of uncertainties and risks can expose a new trade-off space between average-case performance and the amount of uncertainty/risk a design is exposed to. The framework is then generalized and we design a new modeling language that systematically support such analysis through a combination of symbolic execution, graph transformation and compiler optimizations, followed by demonstration of the applicability and benefits of such a modeling language as a foundation to build high quality analysis with closed-form models. We then take the exploration down to the most common practice of architecture studies: cycle-accurate simulations. There a new way of quantifying uncertainty and risk while keeping the computational taxing at bay is proposed through the adaptation of generalized polynomial-chaos theories to build accurate surrogate models.Finally, we peak into the future and envision what needs to be researched before quantifying uncertainties and risks become an essential and well-supported practice of architecture design in this new golden era

Understanding and Estimating Uncertainties and Risks in Architecture Design: from Analytical Analysis to Detailed Simulation

Computer architecture is always changing. Now, more than ever, we see deeper vertical integration with domain-specific software, faster emergence of paradigm shifting computing devices and memory technologies, and unprecedented security and privacy vulnerabilities. These changes all present opportunities for innovations in architecture design, and along with them, uncertainties we have to deal with carefully. An uncertainty-aware approach is essential when we design computer architectures and systems for the future, as variations in technology alone has been demonstrated to have the potential of eliminating the performance gain of an entire CMOS generation. To navigate the new dark waters exposed by changes in fabrication, design constraints, and programming models, a clear understanding and rigorous quantification of uncertainties in architecture designs, as well as the risks that come along, is a critical first step.To achieve such a goal in a tremendous space of designs, from the smallest embedded system to the largest warehouse-scale computing infrastructure, from the most well-characterized CMOS technology node to novel devices at the edge of our understanding, we need new systematic supports across the design stack from high level analytical analysis when evaluating design decisions in the early stage to cycle-accurate detailed simulations when projecting actual system performance.This thesis establishes a route to build a new uncertainty and risk aware architecture design process. We first demonstrate how even a very high level definition and understanding of such concepts of uncertainties and risks can expose a new trade-off space between average-case performance and the amount of uncertainty/risk a design is exposed to. The framework is then generalized and we design a new modeling language that systematically support such analysis through a combination of symbolic execution, graph transformation and compiler optimizations, followed by demonstration of the applicability and benefits of such a modeling language as a foundation to build high quality analysis with closed-form models. We then take the exploration down to the most common practice of architecture studies: cycle-accurate simulations. There a new way of quantifying uncertainty and risk while keeping the computational taxing at bay is proposed through the adaptation of generalized polynomial-chaos theories to build accurate surrogate models.Finally, we peak into the future and envision what needs to be researched before quantifying uncertainties and risks become an essential and well-supported practice of architecture design in this new golden era

Study on Gas Migration Mechanism and Multi-Borehole Spacing Optimization in Coal under Negative Pressure Extraction

In order to study the gas migration in gas-bearing coal, and reasonably arrange gas drainage boreholes to improve the efficiency of gas drainage, a gas-solid coupling model is established based on the pore-fracture dual medium porous model. The solid deformation of coal body, gas seepage and diffusion, and gas adsorption and desorption are considered in this model. The COMSOL software is used to simulate the gas change in the coal matrix and coal fracture under single borehole extraction. We analyze the effective extraction range and study the migration mechanism of gas between coal fracture and borehole, coal matrix and coal fracture, and coal matrix. The effective extraction area of multi-borehole negative pressure gas extraction varies with extraction time and borehole spacing. At 140 d, the effective extraction radius is r = 1.3 m, and the spacing of boreholes is 233 r=1.5 m, 2 r=2.6 m,4 m,5 m,and 6 m, respectively. The influence of the equilateral triangle shape of three boreholes on the gas extraction effect is studied. The simulation results show that when three boreholes are extracted for 140 days under different borehole spacing, different gas extraction effects will be affected by a superposition effect. Considering the change in gas pressure, the effect of gas extraction in the effective extraction area, and the safety and cost performance of gas extraction, it is concluded that the optimal hole spacing is 5 m around 140 d. This study aims to provide reference for underground gas drilling layout and reasonable hole spacing

A Two-Step Approach for Evaluating the Dynamic Ultimate Load Capacity of Ship Structures

One important parameter for evaluating the safety and reliability of a ship is o the dynamic ultimate load capacity of ship structures. Because of the importance of this parameter, its determination is essential. In this paper, a novel “two-step” approach for determining the dynamic ultimate load capacity of ship structures is proposed. The main idea of two-step approach is to determine the dynamic ultimate load capacity based on the static ultimate load capacity after accounting for impacts that cause strain on the ship structures. This approach is based on nonlinear finite element method. Here, taking stiffened plate as a case study, the practical application of thus two-step approach is discussed in detail. The results of this approach reveal that the static ultimate load capacity decreases by less than 3% after a stiffened plate is subjected to an impact load whose amplitude corresponds to the dynamic ultimate load capacity. Then, the influence of the impact duration on the failure mode and the effect of the impact load cycles and the impact load sequence on the dynamic ultimate load capacity of the stiffened plate were investigated. Finally, the applicability of the two-step approach to a hull girder is demonstrated. The two-step approach and the conclusions presented in this paper can provide guidance for the evaluation of dynamic ultimate load capacity

Predicting Binding Conformation of Peptide at Hydroxyapatite Interface: Interplay with Biomineral-Peptide Binding Affinity

Synthetic short-length peptides have potential applications in the field of biomedical diagnostic and treatment of bone and teeth related diseases. Such peptide sequences have been identified through phage display to have high hydroxyapatite (HAP) binding affinity. Understanding the structure and chemistry of these hydroxyapatite-binding peptides, especially at the interface with hydroxyapatite bio-mineral is crucial with respect to the rational design of such peptides in biomedical assay. In the present study, the conformation of a list of hydroxyapatite-binding peptides as a determinant to the binding affinity is predicted ab initio by using bioinformatics approach RosettaSurface. Two classes of binding conformations have been detected, one is alpha-helix with side chain undergoing a spatial registration to hydroxyapatite surface atoms, and the other is random coil with flexible conformation. The binding affinity of both conformations is relied upon the tight electrostatic interaction of positively charged lysine residue to phosphate ions on HAP surface. In order to compare the binding affinity between peptide with helix and random coil conformations, steered molecular dynamics (SMD) is applied to the non-equilibrium desorbing process of peptide from surface. The binding force of peptide to both HAP (100) and (001) faces was measured. It is reflected on SMD simulation that the intrinsic secondary structure of HAP-binding peptide largely affects the binding affinity, presumably through the entropy-enthalpy trading mechanism at the interface. We expect our results could complement current understanding on the hypothesis of flexible polyelectrolyte interacting with charged solid surface

Loop Closure Detection for Mobile Robot based on Multidimensional Image Feature Fusion

Loop closure detection is a crucial part of VSLAM. However, the traditional loop closure detection algorithms are difficult to adapt to complex and changeable scenes. In this paper, we fuse Gist features, semantic features and appearance features of the image to detect the loop closures quickly and accurately. Firstly, we take advantage of the fast extraction speed of the Gist feature by using it to screen the loop closure candidate frames. Then, the current frame and the candidate frame are semantically segmented to obtain the mask blocks of various types of objects, and the semantic nodes are constructed to calculate the semantic similarity between them. Next, the appearance similarity between the images is calculated according to the shape of the mask blocks. Finally, based on Gist similarity, semantic similarity and appearance similarity, the image similarity calculation model can be built as the basis for loop closure detection. Experiments are carried out on both public and self-filmed datasets. The results show that our proposed algorithm can detect the loop closure in the scene quickly and accurately when the illumination, viewpoint and object change

Intelligent Energy Management for Plug-in Hybrid Electric Bus with Limited State Space

Tabular Q-learning (QL) can be easily implemented into a controller to realize self-learning energy management control of a plug-in hybrid electric bus (PHEB). However, the “curse of dimensionality” problem is difficult to avoid, as the design space is huge. This paper proposes a QL-PMP algorithm (QL and Pontryagin minimum principle (PMP)) to address the problem. The main novelty is that the difference between the feedback SOC (state of charge) and the reference SOC is exclusively designed as state, and then a limited state space with 50 rows and 25 columns is proposed. The off-line training process shows that the limited state space is reasonable and adequate for the self-learning; the Hardware-in-Loop (HIL) simulation results show that the QL-PMP strategy can be implemented into a controller to realize real-time control, and can on average improve the fuel economy by 20.42%, compared to the charge depleting–charge sustaining (CDCS) strategy

Cesium Carbonate-Catalyzed Reduction of Amides with Hydrosilanes

Cesium carbonate has been found to be an effective catalyst for the reduction of tertiary carboxamides with the simple, commercially available PhSiH₃ under solvent-free conditions. The catalytic system can effectively reduce a range of amides under relatively mild conditions (from room temperature to 80 °C) to yield the corresponding amines in good to excellent yields (71–100%) and thus has the potential for practical applications