Robust near-threshold QDI circuit analysis and design

The two most important digital-system design goals today are to reduce power consumption and to increase reliability. Reductions in power consumption improve battery life in the mobile space and reductions in energy lower operating costs in the datacenter. Increased robustness and reliability shorten down time, improve yield, and are invaluable in the context of safety-critical systems. While optimizing towards these two goals is important at all design levels, optimizations at the circuit level have the furthest reaching effects; they apply to all digital systems. This dissertation presents a study of robust minimum-energy digital circuit design and analysis. It introduces new device models, metrics, and methods of calculation—all necessary first steps towards building better systems—and demonstrates how to apply these techniques. It analyzes a fabricated chip (a full-custom QDI microcontroller designed at Caltech and taped-out in 40-nm silicon) by calculating the minimum energy operating point and quantifying the chip’s robustness in the face of both timing and functional failures.

Optimal design of building structures using genetic algorithms

A general framework for multi-criteria optimal design is presented which is well-suited for automated design of structural systems. A systematic computer-aided optimal design decision process is developed which allows the designer to rapidly evaluate and improve a proposed design by taking into account the major factors of interest related to different aspects such as design, construction, and operation.

The proposed optimal design process requires the selection of the most promising choice of design parameters taken from a large design space, based on an evaluation using specified criteria. The design parameters specify a particular design, and so they relate to member sizes, structural configuration, etc. The evaluation of the design uses performance parameters which may include structural response parameters, risks due to uncertain loads and modeling errors, construction and operating costs, etc. Preference functions are used to implement the design criteria in a "soft" form. These preference functions give a measure of the degree of satisfaction of each design criterion. The overall evaluation measure for a design is built up from the individual measures for each criterion through a preference combination rule. The goal of the optimal design process is to obtain a design that has the highest overall evaluation measure - an optimization problem.

Genetic algorithms are stochastic optimization methods that are based on evolutionary theory. They provide the exploration power necessary to explore high-dimensional search spaces to seek these optimal solutions. Two special genetic algorithms, hGA and vGA, are presented here for continuous and discrete optimization problems, respectively.

The methodology is demonstrated with several examples involving the design of truss and frame systems. These examples are solved by using the proposed hGA and vGA.

Stress and load calculation for unknown building

Not available.

The effect of earthquakes on reinforced concrete building design

The building under consideration was the Pasadena Furniture Building, on East Colorado St., Pasadena, California. It is a modern reinforced concrete building that may be classed as a warehouse of flat-slab construction.