A framework to explore low-power architecture and variability-aware timing estimation of FPGAs

Master'sMASTER OF ENGINEERIN

Statistical Yield Analysis and Design for Nanometer VLSI

Process variability is the pivotal factor impacting the design of high yield integrated circuits and systems in deep sub-micron CMOS technologies. The electrical and physical properties of transistors and interconnects, the building blocks of integrated circuits, are prone to significant variations that directly impact the performance and power consumption of the fabricated devices, severely impacting the manufacturing yield. However, the large number of the transistors on a single chip adds even more challenges for the analysis of the variation effects, a critical task in diagnosing the cause of failure and designing for yield. Reliable and efficient statistical analysis methodologies in various design phases are key to predict the yield before entering such an expensive fabrication process. In this thesis, the impacts of process variations are examined at three different levels: device, circuit, and micro-architecture. The variation models are provided for each level of abstraction, and new methodologies are proposed for efficient statistical analysis and design under variation. At the circuit level, the variability analysis of three crucial sub-blocks of today's system-on-chips, namely, digital circuits, memory cells, and analog blocks, are targeted. The accurate and efficient yield analysis of circuits is recognized as an extremely challenging task within the electronic design automation community. The large scale of the digital circuits, the extremely high yield requirement for memory cells, and the time-consuming analog circuit simulation are major concerns in the development of any statistical analysis technique. In this thesis, several sampling-based methods have been proposed for these three types of circuits to significantly improve the run-time of the traditional Monte Carlo method, without compromising accuracy. The proposed sampling-based yield analysis methods benefit from the very appealing feature of the MC method, that is, the capability to consider any complex circuit model. However, through the use and engineering of advanced variance reduction and sampling methods, ultra-fast yield estimation solutions are provided for different types of VLSI circuits. Such methods include control variate, importance sampling, correlation-controlled Latin Hypercube Sampling, and Quasi Monte Carlo. At the device level, a methodology is proposed which introduces a variation-aware design perspective for designing MOS devices in aggressively scaled geometries. The method introduces a yield measure at the device level which targets the saturation and leakage currents of an MOS transistor. A statistical method is developed to optimize the advanced doping profiles and geometry features of a device for achieving a maximum device-level yield. Finally, a statistical thermal analysis framework is proposed. It accounts for the process and thermal variations simultaneously, at the micro-architectural level. The analyzer is developed, based on the fact that the process variations lead to uncertain leakage power sources, so that the thermal profile, itself, would have a probabilistic nature. Therefore, by a co-process-thermal-leakage analysis, a more reliable full-chip statistical leakage power yield is calculated

Identification of Influential Climate Indicators, Prediction of Long-Term Streamflow and Great Salt Lake Elevation Using Machine Learning Approach

To meet the surging water demand due to rapid population growth and changing climatic conditions around the world, and to reduce the impact of floods and droughts, comprehensive water management and planning is necessary. Climatic variability, hydrologic uncertainty and variability of hydrologic quantities in time and space are inherent to hydrological modeling. Hydrologic modeling using a physically-based model can be very complex and typically requires detailed knowledge of physical processes. The availability of data is an important issue to justify the use of these models. Data-driven models are an alternative choice. This is a relatively new and efficient approach to modeling. Data-drive models bridge the gap between the classical regression and physically-based models. By using a data-driven model that relies on the machine learning approach, it is possible to produce reasonable predictions from a limited data set and limited knowledge of underlying physical processes of the system by just relating input and output. This dissertation uses the Multivariate Relevance Vector Machine (MVRVM) and Support Vector Machine (SVM) for predicting a variety of hydrological quantities. These models are used in this dissertation for identifying influential climate indicators, and are used for long-term streamflow prediction for multiple lead times at different locations in Utah. They are also used for prediction of Great Salt Lake (GSL) elevation series. They provide reasonable predictions of hydrological quantities from the available data. The predictions from these models are robust and parsimonious. This research presents the first attempt to identify influential climate indicators and predict long lead-time streamflow in Utah, and to predict lake elevation using machine learning models. The approach presented herein has potential value for water resources planning and management especially for irrigation and flood management

Cross-spectral study of the spatial relationships in the North Pacific sea-suface temperature anomaly field, A

Includes bibliographical references.March 1980.C00-1340-68

Heartbeat of the Southern Oscillation explains ENSO climatic resonances

This is the final version. Available from the publisher via the DOI in this record.The El Nino-Southern Oscillation (ENSO) nonlinear oscillator phenomenon has a far reaching ~ influence on the climate and human activities. The up to 10 year quasi-period cycle of the El Nino and ~ subsequent La Nina is known to be dominated in the tropics by nonlinear physical interaction of wind with ~ the equatorial waveguide in the Pacific. Long-term cyclic phenomena do not feature in the current theory of the ENSO process. We update the theory by assessing low (>10 years) and high (<10 years) frequency coupling using evidence across tropical, extratropical, and Pacific basin scales. We analyze observations and model simulations with a highly accurate method called Dominant Frequency State Analysis (DFSA) to provide evidence of stable ENSO features. The observational data sets of the Southern Oscillation Index (SOI), North Pacific Index Anomaly, and ENSO Sea Surface Temperature Anomaly, as well as a theoretical model all confirm the existence of long-term and short-term climatic cycles of the ENSO process with resonance frequencies of {2.5, 3.8, 5, 12–14, 61–75, 180} years. This fundamental result shows long-term and short-term signal coupling with mode locking across the dominant ENSO dynamics. These dominant oscillation frequency dynamics, defined as ENSO frequency states, contain a stable attractor with three frequencies in resonance allowing us to coin the term Heartbeat of the Southern Oscillation due to its characteristic shape. We predict future ENSO states based on a stable hysteresis scenario of short-term and long-term ENSO oscillations over the next century.Natural Environment Research Council (NERC)Plymouth Marine Laboratory (PML

Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in australia

Australia is an agricultural nation characterised by one of the most naturally diverse climates in the world, which translates into significant sources of risk for agricultural production and subsequent farm revenues. Extreme climatic events have been significantly affecting large parts of Australia in recent decades, contributing to an increase in the vulnerability of crops, and leading to subsequent higher risk to a large number of agricultural producers. However, attempts at better managing climate related risks in the agricultural sector have confronted many challenges. First, crop insurance products, including classical claim-based and index-based insurance, are among the financial implements that allow exposed individuals to pool resources to spread their risk. The classical claim-based insurance indemnifies according to a claim of crop loss from the insured customer, and so can easily manage idiosyncratic risk, which is the case where the loss occurs independently.Nevertheless, the existence of systemic weather risk (covariate risk), which is the spread of extreme events over locations and times (e.g., droughts and floods), has been identified as the main reason for the failure of private insurance markets, such as the classical multi-peril crop insurance, for agricultural crops. The index-based insurance is appropriate to handle systemic but not idiosyncratic risk. The indemnity payments of the index-based insurance are triggered by a predefined threshold of an index (e.g., rainfall), which is related to such losses. Since the covariate nature of a climatic event, it sanctions the insurers to predict losses and ascertain indemnifications for a huge number of insured customers across a wide geographical area. However, basis risk, which is related to the strength of the relationship between the predefined indices used to estimate the average loss by the insured community and the actual loss of insured assets by an individual, is a major barrier that hinders uptake of the index-based insurance. Clearly, the high basis risk, which is a weak relationship between the index and loss, destroys the willingness of potential customers to purchase this insurance product. Second, the impact of multiple synoptic-scale climate mode indices (e.g., Southern Oscillation Index (SOI) and Indian Ocean Index (IOD)) on precipitation and crop yield is not identical in different spatial locations and at different times or seasons across the Australian continent since the influence of large-scale climate heterogeneous over the different regions. The occurrence, role, and amplitude of synoptic-scale climate modes contributing to the variability of seasonal crop production have shifted in recent decades. These variables generally complicate the climate and crop yield relationship that cannot be captured by traditional modelling and analysis approaches commonly found in published agronomic literature such as linear regression. In addition, the traditional linear analysis is not able to model the nonlinear and asymmetric interdependence between extreme insurance losses, which may occur in the case of systemic risk. Relying on the linear method may lead to the problem that different behaviour may be observed from joint distributions, particularly in the upper and lower regions, with the same correlation coefficient. As a result, the likelihood of extreme insurance losses can be underestimated or overestimated that lead to inaccuracies in the pricing of insurance policies. Another alternative is the use of the multivariate normal distribution, where the joint distribution is uniquely defined using the marginal distributions of variables and their correlation matrix. However, phenomena are not always normally distributed in practice. It is therefore important to develop new, scientifically verified, strategic measures to solve the challenges as mentioned above in order to support mitigating the influences of the climate-related risk in the agricultural sector. Copulas provide an advanced statistical approach to model the joint distribution of multivariate random variables. This technique allows estimating the marginal distributions of individual variables independently with their dependence structures. It is clear that the copula method is superior to the conventional linear regression since it does not require variables have to be normally distributed and their correlation can be either linear or non-linear. This doctoral thesis therefore adopts the advanced copula technique within a statistical modelling framework that aims to model: (1) The compound influence of synoptic-scale climate indices (i.e., SOI and IOD) and climate variables (i.e., precipitation) to develop a probabilistic precipitation forecasting system where the integrated role of different factors that govern precipitation dynamics are considered; (2) The compound influence of synoptic-scale climate indices on wheat yield; (3) The scholastic interdependencies of systemic weather risks where potential adaptation strategies are evaluated accordingly; and (4) The risk-reduction efficiencies of geographical diversifications in wheat farming portfolio optimisation. The study areas are Australia’s agro-ecological (i.e., wheat belt) zones where major seasonal wheat and other cereal crops are grown. The results from the first and second objectives can be used for not only forecasting purposes but also understanding the basis risk in the case of pricing climate index-based insurance products. The third and fourth objectives assess the interactions of drought events across different locations and in different seasons and feasible adaptation tools. The findings of these studies can provide useful information for decision-makers in the agricultural sector. The first study found the significant relationship between SOI, IOD, and precipitation. The results suggest that spring precipitation in Australia, except for the western part, can be probabilistically forecasted three months ahead. It is more interesting that the combination of SOI and IOD as the predictors will improve the performance of the forecast model. Similarly, the second study indicated that the largescale climate indices could provide knowledge of wheat crops up to six months in advance. However, it is noted that the influence of different climate indices varies over locations and times. Furthermore, the findings derived from the third study demonstrated the spatio-temporally stochastic dependence of the drought events. The results also prove that time diversification is potentially more effective in reducing the systemic weather risk compared to spatially diversifying strategy. Finally, the fourth objective revealed that wheat-farming portfolio could be effectively optimised through the geographical diversification. The outcomes of this study will lead to the new application of advanced statistical tools that provide a better understanding of the compound influence of synoptic-scale climatic conditions on seasonal precipitation, and therefore on wheat crops in key regions over the Australian continent. Furthermore, a comprehensive analysis of systemic weather risks performed through advanced copula-statistical models can help improve and develop novel agricultural adaptation strategies in not only the selected study region but also globally, where climate extreme events pose a serious threat to the sustainability and survival of the agricultural industry. Finally, the evaluation of the effectiveness of diversification strategies implemented in this study reveals new evidence on whether the risk pooling methods could potentially mitigate climate risks for the agricultural sector and subsequently, help farmers in prior preparation for uncertain climatic events

Climate-Based Emulator of Distant Swell Trains and Local Seas Approaching a Pacific Atoll

ABSTRACT: Wave-induced flooding is a major coastal hazard for the low-lying atolls of the Pacific. These flooding events are expected to increase over time, which may cause significant coastal damage in some locations. Coastal flooding analysis (forensic or forecasted) is particularly challenging in these small islands due to the co-occurrence of several swells and local seas propagating in a complex configuration of archipelagos. Therefore, assessing the contribution of swells and wind seas on the flooding hazards that threaten the atoll islands requires the spectral characterization of the wave climate, since integrated wave parameters do not accurately represent the wave conditions in these environments. On the other hand, the relative short records of wave conditions, represent only a small fraction of the possible range of combinations that could produce a wave-induced flooding event. For these reasons, we propose the analysis of all the spectral energy arriving toward a study site, by isolating and parameterizing each swell train. Then, taking into account the link with large-scale climatic patterns (i.e., El Niño Southern Oscillation), we present a new multi-modal seas emulator capable of generating infinitely long time series of synthetic individual swell trains and seas. This new climate-based emulator allows a better understanding of swell behavior in the Pacific, and the generation of multimodal wave conditions to populate the historical records as a key point to perform robust coastal flood risk assessments considering climate variability

Non-invasive IC tomography using spatial correlations

We introduce a new methodology for post-silicon characterization of the gate-level variations in a manufactured Integrated Circuit (IC). The estimated characteristics are based on the power and the delay measurements that are affected by the process variations. The power (delay) variations are spatially correlated. Thus, there exists a basis in which variations are sparse. The sparse representation suggests using the L1-regularization (the compressive sensing theory). We show how to use the compressive sensing theory to improve post-silicon characterization. We also address the problem by adding spatial constraints directly to the traditional L2-minimization. The proposed methodology is fast, inexpensive, non-invasive, and applicable to legacy designs. Noninvasive IC characterization has a range of emerging applications, including post-silicon optimization, IC identification, and variations' modeling/simulations. The evaluation results on standard benchmark circuits show that, in average, the gate level characteristics estimation accuracy can be improved by more than two times using the proposed methods

Understanding and Predicting Changes in Precipitation and Water Availability Under the Influence of Large-Scale Circulation Patterns: Rio Grande and Texas

Large-scale circulation patterns have a significant modulating influence on local hydro-meteorological variables, and consequently on water availability. An understanding of the influence of these patterns on the hydrological cycle, and the ability to timely predict their impacts, is crucial for water resources planning and management. This dissertation focusses on the influence of two major large-scale circulation patterns, the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on the Rio Grande basin and the state of Texas, US. Both study areas are subject to a varying climate, and are extremely vulnerable to droughts, which can have devastating socio-economic impacts. The strength and spatial correlation structure of the climate indices on gauged precipitation was first established. Precipitation is not linearly related to water availability; therefore a land surface model (LSM), with land use land cover constant, was used to create naturalized flow, as it incorporates all necessary hydro-meteorological factors. As not all ENSO events are created equal, the influence of individual El Niño and La Niña events, classified using four different metrics, on water availability was examined. A general increase (decrease) in runoff during El Niños (La Niñas) was noted, but some individual events actually caused a decrease (increase) in water availability. Long duration El Niños have more influence on water availability than short duration high intensity events. Positive PDO enhances the effect of El Niño, and dampens the negative effect of La Niña, but when it is in its neutral or transition phase, La Niña tends to dominate climatic conditions and reduce water availability. LSM derived runoffs were converted into 3-month Standardized Runoff Indices (SRI 3) from which water deficit durations and severities were extracted. Conditional probability models of duration and severity were developed and compared with that based on observed precipitations. It was found that model derived information can be used in regions having limited ground observation data, or can be used in tandem with observation driven conditional probabilities for more efficient water resources planning and management. Finally a multidimensional model was developed, using copulas, to predict precipitation based on the phase of ENSO and PDO. A bivariate model, with ENSO and precipitation, was compared to a trivariate model, which incorporates PDO, and it was found that information on the state of PDO is important for efficient precipitation predictions