Design and Verification of Clock Domain Crossing Interfaces

The clock distribution network is an essential component in every synchronous digital system. The design of this network is becoming an increasingly sophisticated and difficult task due to the increasing logic capacity of chips and due to the fact that this network has to reach out to each and every memory element in the chip. Multiclock domain circuits with Clock Domain Crossing (CDC) interfaces are emerging as an alternative to circuits with a global clock. The design of CDC interfaces is a challenging task due to the difficulty of dealing with two possibly unrelated clock domains and the possibility of propagating metastability into the communicating blocks making CDC interfaces difficult to design and verify. In this work, we present a hybrid FIFO-asynchronous method for constructing robust CDC interfaces. This method avoids the shortcomings of previous interfaces and provides reliable transfer of data and control signals between different clock domains. A complete design is proposed, fully implemented using 90nm TSMC CMOS technology, and simulated using SPICE. Extensive simulations confirmed the robustness of the interface at different temperatures, different workloads, and varying frequency ratios. The reported implementation provides a maximum throughput of 606 Mitems/s. Moreover, we also address the challenging task of the verification of CDC interfaces. Most RTL simulation tools available today are incapable of simulating these interfaces. In this thesis, we present a framework for the formal verification of CDC interfaces. The framework explicitly models metastability by taking advantage of the unique features of probabilistic model checking. The framework is applied to common CDC interfaces by verifying them using the PRISM model checker

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere

Design and scheduling of effcient real-time embedded systems

Computer systems have gone through tremendous changes in the past fewdecades. Relatively large general purpose computers dominated the early daysof computers. With time, demand increased for smaller, more dedicated computersystems, called embedded systems. These systems perform a specificset of functions interacting with the physical environment, often in real-time.Real-time embedded systems are found today in many application domainssuch as the automotive domain, avionics, and control systems. Real-time systemsdiffer from traditional computer systems in their dependence on time asa correctness criteria, i.e., a late correct answer is useless for these systems.Embedded real-time systems today are more integrated, more parallel, andmore complex than ever before. In this thesis, we discuss limitations thataffect the applicability of real-time models, analysis methods, and schedulingapproaches to the realities of today's embedded systems and propose solutionsto address these challenges. We first look into the issue of shared resources and its effect on the mapping and scheduling of software tasks in a real-time system. Most task mappingapproaches proposed in the literature perform task mapping assuming independenttasks that do not share resources. Managing shared resources andtheir protection mechanisms is performed later. However, this approach mightrequire several rounds of iteration and can lead to inefficient results. In thisthesis, we explore the possibility of using different resource protection mechanismswithin a single system, and propose to tackle the design problem moreefficiently by jointly performing task allocation, scheduling, and resource protection mechanism selection. Two approaches are presented to solve this optimizationproblem: an optimal Mixed Integer Linear Programming (MILP)approach and an efficient heuristic. The proposed work is shown to significantly improve system schedulability. Experimental results indicate that theminimum utilization at which at least 95% of systems become scheulable canbe improved from 65%-70% for the best published task allocation algorithmsto 76%-85% using our heuristic with minimal memory cost. Even better resultscan be achieved using the MILP approach.Next, we look into the design of systems composed of components thathave different levels of criticality. Mixed-Criticality Systems (MCS) receivedmuch attention recently to due their industrial relevance. We focus on threechallenges in MCS design: task allocation, fault-tolerance, and model-baseddesign. For task allocation, we show that traditional task allocation algorithmscan be inefficient in a mixed-criticality context, and propose an alternative thatwe call dual-partitioned task allocation. Experiments show that for systemsthat have a utilization of 80% or higher, we can schedule 17% more systemson a given multicore platform using the dual-partitioned approach. Fault-tolerance is an important issue for MCS since these systems containa safety critical part. To design MCS that tolerate hardware transient faults,we propose a new mixed-criticality model that simultaneously addresses criticality,reliability, and Quality of Service (QoS). A schedulability test for thenew model is derived. Furthermore, to allow designers to incorporate the newmodel and analysis in their design process, we propose a design space explorationframework based on the new model that supports various fault-tolerancemechanisms. QoS improvements of up to 42.9% can be achieved using the newmodel compared to the traditional MCS model extended to support transientfaults.For model-based design, we propose algorithms to generate optimized semantic-preserving implementationsfor MCS specified using the SR model, with minimal functional delay addition.An optimal Branch-and-Bound based algorithm and an efficient heuristicare proposed for this purpose.Les systemes informatiques ont subi des changements enormes au cours des dernieres decennies. Dans leurs debuts, les ordinateurs, de grande taille et a usage general, etaient dominants. Avec le temps, la demande pour des systemes informatiques plus petits et dedies pour des taches plus specifique, appeles systemes embarques, a augmente. Ces systemes executent un ensemble de fonctions specifique interagissant avec l'environnement physique, souvent en temps reel. Les systemes embarques temps-reel se trouvent aujourd'hui dans de nombreux domaines d'application tels que l'automobile, l'avionique et les systemes de controle. Les systemes temps-reel different des systemes informatiques traditionnels dans leur dependence au temps qui est utilise comme critere de correction. C'est-a-dire qu'une reponse correcte tardive est inutile pour ces systemes. Les systemes embarques temps-reel sont aujourd'hui plus integres, plus paralleles et plus complexes que jamais. Dans cette these, nous discutons des limites qui affectent l'applicabilite des modeles temps-reel, des methodes d'analyse et des approches d'ordonnancement aux realites des systemes embarques d'aujourd'hui et nous proposons des solutions pour relever ces defis. En premier lieu, nous examinons la question des ressources partagees et leurs effets sur la cartographie et l'ordonnancement des taches logicielles dans un systeme temps-reel. La plupart des approches de cartographie des taches proposees dans la litterature effectuent la cartographie des taches en assumant des taches independantes qui ne partagent pas les ressources. La gestion des ressources partagees ainsi que leurs mecanismes de protection sont effectues plus tard. Cependant, cette approche peut necessiter plusieurs cycles d'iteration et peut mener a des resultats inefficaces. Dans cette these, nous explorons la possibilite d'utiliser differnents mecanismes de protection des ressources au sein d'un meme systeme et proposons d'aborder plus efficacement le probleme de conception en executant conjointement l'attribution des taches, l'ordonnancement et la selection des mecanismes de protection des ressources. Deux approches sont presentees pour resoudre ce probleme d'optimisation: une approche doptimisation lineaire a nombres entiers mixtes optimale (MILP) etune heuristique efficace. Le travail propose permet d'ameliorer considerablement lordonnancabilite du systeme. Les resultats experimentaux indiquent que l'utilisation minimale a laquelle au moins 95% des systemes deviennent ordonnancables peut etre amelioree de 65%-70%, dans les meilleurs algorithmes d'allocation de tache publies, a 76%-85% en utilisant notre heuristique avec un cot memoire minime. Des resultats encore meilleurs peuvent etre obtenus en utilisant l'approche MILP. Ensuite, nous examinons la conception de systemes formes de composants qui ont differents niveaux de criticite. Nous nous concentrons sur trois defis en matiere de conception MCS: l attribution des taches, la tolerance aux pannes et la conception basee sur modele. Pour l'attribution des taches, nous proposons l'allocation detaches a double partition. Les experiences montrent que pour les systemes dont l'utilisation est superieure ou egale a 80%, qu'on peut ordonnancer 17% de systemes sur une plate-forme multicur donnee en utilisant l'approche a deux partitions. Pour concevoir des MCS qui tolerent les defauts transitoires, nous proposons un nouveau modele de criticite mixte qui aborde simultanement la criticite, la abilite et la qualite de service (QoS). Nous proposons un cadre d'exploration d'espace de conception base sur le nouveau modele qui prend encharge divers mecanismes de tolerance de pannes. Des ameliorations de la qualite de service jusqu'a 42.9% peuvent être obtenues