Analyzing and Predicting Processor Vulnerability to Soft Errors Using Statistical Techniques

The shrinking processor feature size, lower threshold voltage and increasing on-chip transistor density make current processors highly vulnerable to soft errors. Architectural Vulnerability Factor (AVF) reflects the probability that a raw soft error eventually causes a visible error in the program output, indicating the processor’s susceptibility to soft errors at architectural level. The awareness of the AVF, both at the early design stage and during program runtime, is greatly useful for designing reliable processors. However, measuring the AVF is extremely costly, resulting in large overheads in hardware, computation, and power. The situation is further exacerbated in a multi-threaded processor environment where resource contention and data sharing exist among different threads. Consequently, predicting the AVF from other easily-measured metrics becomes extraordinarily attractive to computer designers. We propose a series of AVF modeling and prediction works via using advanced statistical techniques. First, we utilize the Boosted Regression Trees (BRT) scheme to dynamically predict the AVF during program execution from a variety of performance metrics. This correlation is generalized to be across different workloads, program phases, and processor configurations on a single-threaded superscalar processor. Second, the AVF prediction is extended to multi-threaded processors where the inter-thread resource contention shows significant and non-uniform impacts on different programs; we propose a two-level predictive mechanism using BRT as building blocks to characterize the contention behavior. Finally, we employ a rule search strategy named Patient Rule Induction Method (PRIM) to explore a large processor design space at the early design stage. We are capable of generating selective rules on important configuration parameters. These rules quantify the design space subregion yielding lowest values of the response, thereby providing useful guidelines for designing reliable processors while achieving high performance

Mechanistic modeling of architectural vulnerability factor

Reliability to soft errors is a significant design challenge in modern microprocessors owing to an exponential increase in the number of transistors on chip and the reduction in operating voltages with each process generation. Architectural Vulnerability Factor (AVF) modeling using microarchitectural simulators enables architects to make informed performance, power, and reliability tradeoffs. However, such simulators are time-consuming and do not reveal the microarchitectural mechanisms that influence AVF. In this article, we present an accurate first-order mechanistic analytical model to compute AVF, developed using the first principles of an out-of-order superscalar execution. This model provides insight into the fundamental interactions between the workload and microarchitecture that together influence AVF. We use the model to perform design space exploration, parametric sweeps, and workload characterization for AVF

PerfWeb: How to Violate Web Privacy with Hardware Performance Events

The browser history reveals highly sensitive information about users, such as financial status, health conditions, or political views. Private browsing modes and anonymity networks are consequently important tools to preserve the privacy not only of regular users but in particular of whistleblowers and dissidents. Yet, in this work we show how a malicious application can infer opened websites from Google Chrome in Incognito mode and from Tor Browser by exploiting hardware performance events (HPEs). In particular, we analyze the browsers' microarchitectural footprint with the help of advanced Machine Learning techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines, and in contrast to previous literature also Convolutional Neural Networks. We profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing portals, on two machines featuring an Intel and an ARM processor. By monitoring retired instructions, cache accesses, and bus cycles for at most 5 seconds, we manage to classify the selected websites with a success rate of up to 86.3%. The results show that hardware performance events can clearly undermine the privacy of web users. We therefore propose mitigation strategies that impede our attacks and still allow legitimate use of HPEs

Decompose and Conquer: Addressing Evasive Errors in Systems on Chip

Modern computer chips comprise many components, including microprocessor cores, memory modules, on-chip networks, and accelerators. Such system-on-chip (SoC) designs are deployed in a variety of computing devices: from internet-of-things, to smartphones, to personal computers, to data centers. In this dissertation, we discuss evasive errors in SoC designs and how these errors can be addressed efficiently. In particular, we focus on two types of errors: design bugs and permanent faults. Design bugs originate from the limited amount of time allowed for design verification and validation. Thus, they are often found in functional features that are rarely activated. Complete functional verification, which can eliminate design bugs, is extremely time-consuming, thus impractical in modern complex SoC designs. Permanent faults are caused by failures of fragile transistors in nano-scale semiconductor manufacturing processes. Indeed, weak transistors may wear out unexpectedly within the lifespan of the design. Hardware structures that reduce the occurrence of permanent faults incur significant silicon area or performance overheads, thus they are infeasible for most cost-sensitive SoC designs. To tackle and overcome these evasive errors efficiently, we propose to leverage the principle of decomposition to lower the complexity of the software analysis or the hardware structures involved. To this end, we present several decomposition techniques, specific to major SoC components. We first focus on microprocessor cores, by presenting a lightweight bug-masking analysis that decomposes a program into individual instructions to identify if a design bug would be masked by the program's execution. We then move to memory subsystems: there, we offer an efficient memory consistency testing framework to detect buggy memory-ordering behaviors, which decomposes the memory-ordering graph into small components based on incremental differences. We also propose a microarchitectural patching solution for memory subsystem bugs, which augments each core node with a small distributed programmable logic, instead of including a global patching module. In the context of on-chip networks, we propose two routing reconfiguration algorithms that bypass faulty network resources. The first computes short-term routes in a distributed fashion, localized to the fault region. The second decomposes application-aware routing computation into simple routing rules so to quickly find deadlock-free, application-optimized routes in a fault-ridden network. Finally, we consider general accelerator modules in SoC designs. When a system includes many accelerators, there are a variety of interactions among them that must be verified to catch buggy interactions. To this end, we decompose such inter-module communication into basic interaction elements, which can be reassembled into new, interesting tests. Overall, we show that the decomposition of complex software algorithms and hardware structures can significantly reduce overheads: up to three orders of magnitude in the bug-masking analysis and the application-aware routing, approximately 50 times in the routing reconfiguration latency, and 5 times on average in the memory-ordering graph checking. These overhead reductions come with losses in error coverage: 23% undetected bug-masking incidents, 39% non-patchable memory bugs, and occasionally we overlook rare patterns of multiple faults. In this dissertation, we discuss the ideas and their trade-offs, and present future research directions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147637/1/doowon_1.pd

Using machine learning techniques to evaluate multicore soft error reliability

Virtual platform frameworks have been extended to allow earlier soft error analysis of more realistic multicore systems (i.e., real software stacks, state-of-the-art ISAs). The high observability and simulation performance of underlying frameworks enable to generate and collect more error/failurerelated data, considering complex software stack configurations, in a reasonable time. When dealing with sizeable failure-related data sets obtained from multiple fault campaigns, it is essential to filter out parameters (i.e., features) without a direct relationship with the system soft error analysis. In this regard, this paper proposes the use of supervised and unsupervised machine learning techniques, aiming to eliminate non-relevant information as well as identify the correlation between fault injection results and application and platform characteristics. This novel approach provides engineers with appropriate means that able are able to investigate new and more efficient fault mitigation techniques. The underlying approach is validated with an extensive data set gathered from more than 1.2 million fault injections, comprising several benchmarks, a Linux OS and parallelization libraries (e.g., MPI, OpenMP), as well as through a realistic automotive case study

New Techniques for On-line Testing and Fault Mitigation in GPUs

L'abstract è presente nell'allegato / the abstract is in the attachmen

Power Modeling and Resource Optimization in Virtualized Environments

The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm

New Fault Detection, Mitigation and Injection Strategies for Current and Forthcoming Challenges of HW Embedded Designs

Tesis por compendio[EN] Relevance of electronics towards safety of common devices has only been growing, as an ever growing stake of the functionality is assigned to them. But of course, this comes along the constant need for higher performances to fulfill such functionality requirements, while keeping power and budget low. In this scenario, industry is struggling to provide a technology which meets all the performance, power and price specifications, at the cost of an increased vulnerability to several types of known faults or the appearance of new ones. To provide a solution for the new and growing faults in the systems, designers have been using traditional techniques from safety-critical applications, which offer in general suboptimal results. In fact, modern embedded architectures offer the possibility of optimizing the dependability properties by enabling the interaction of hardware, firmware and software levels in the process. However, that point is not yet successfully achieved. Advances in every level towards that direction are much needed if flexible, robust, resilient and cost effective fault tolerance is desired. The work presented here focuses on the hardware level, with the background consideration of a potential integration into a holistic approach. The efforts in this thesis have focused several issues: (i) to introduce additional fault models as required for adequate representativity of physical effects blooming in modern manufacturing technologies, (ii) to provide tools and methods to efficiently inject both the proposed models and classical ones, (iii) to analyze the optimum method for assessing the robustness of the systems by using extensive fault injection and later correlation with higher level layers in an effort to cut development time and cost, (iv) to provide new detection methodologies to cope with challenges modeled by proposed fault models, (v) to propose mitigation strategies focused towards tackling such new threat scenarios and (vi) to devise an automated methodology for the deployment of many fault tolerance mechanisms in a systematic robust way. The outcomes of the thesis constitute a suite of tools and methods to help the designer of critical systems in his task to develop robust, validated, and on-time designs tailored to his application.[ES] La relevancia que la electrónica adquiere en la seguridad de los productos ha crecido inexorablemente, puesto que cada vez ésta copa una mayor influencia en la funcionalidad de los mismos. Pero, por supuesto, este hecho viene acompañado de una necesidad constante de mayores prestaciones para cumplir con los requerimientos funcionales, al tiempo que se mantienen los costes y el consumo en unos niveles reducidos. En este escenario, la industria está realizando esfuerzos para proveer una tecnología que cumpla con todas las especificaciones de potencia, consumo y precio, a costa de un incremento en la vulnerabilidad a múltiples tipos de fallos conocidos o la introducción de nuevos. Para ofrecer una solución a los fallos nuevos y crecientes en los sistemas, los diseñadores han recurrido a técnicas tradicionalmente asociadas a sistemas críticos para la seguridad, que ofrecen en general resultados sub-óptimos. De hecho, las arquitecturas empotradas modernas ofrecen la posibilidad de optimizar las propiedades de confiabilidad al habilitar la interacción de los niveles de hardware, firmware y software en el proceso. No obstante, ese punto no está resulto todavía. Se necesitan avances en todos los niveles en la mencionada dirección para poder alcanzar los objetivos de una tolerancia a fallos flexible, robusta, resiliente y a bajo coste. El trabajo presentado aquí se centra en el nivel de hardware, con la consideración de fondo de una potencial integración en una estrategia holística. Los esfuerzos de esta tesis se han centrado en los siguientes aspectos: (i) la introducción de modelos de fallo adicionales requeridos para la representación adecuada de efectos físicos surgentes en las tecnologías de manufactura actuales, (ii) la provisión de herramientas y métodos para la inyección eficiente de los modelos propuestos y de los clásicos, (iii) el análisis del método óptimo para estudiar la robustez de sistemas mediante el uso de inyección de fallos extensiva, y la posterior correlación con capas de más alto nivel en un esfuerzo por recortar el tiempo y coste de desarrollo, (iv) la provisión de nuevos métodos de detección para cubrir los retos planteados por los modelos de fallo propuestos, (v) la propuesta de estrategias de mitigación enfocadas hacia el tratamiento de dichos escenarios de amenaza y (vi) la introducción de una metodología automatizada de despliegue de diversos mecanismos de tolerancia a fallos de forma robusta y sistemática. Los resultados de la presente tesis constituyen un conjunto de herramientas y métodos para ayudar al diseñador de sistemas críticos en su tarea de desarrollo de diseños robustos, validados y en tiempo adaptados a su aplicación.[CA] La rellevància que l'electrònica adquireix en la seguretat dels productes ha crescut inexorablement, puix cada volta més aquesta abasta una major influència en la funcionalitat dels mateixos. Però, per descomptat, aquest fet ve acompanyat d'un constant necessitat de majors prestacions per acomplir els requeriments funcionals, mentre es mantenen els costos i consums en uns nivells reduïts. Donat aquest escenari, la indústria està fent esforços per proveir una tecnologia que complisca amb totes les especificacions de potència, consum i preu, tot a costa d'un increment en la vulnerabilitat a diversos tipus de fallades conegudes, i a la introducció de nous tipus. Per oferir una solució a les noves i creixents fallades als sistemes, els dissenyadors han recorregut a tècniques tradicionalment associades a sistemes crítics per a la seguretat, que en general oferixen resultats sub-òptims. De fet, les arquitectures empotrades modernes oferixen la possibilitat d'optimitzar les propietats de confiabilitat en habilitar la interacció dels nivells de hardware, firmware i software en el procés. Tot i això eixe punt no està resolt encara. Es necessiten avanços a tots els nivells en l'esmentada direcció per poder assolir els objectius d'una tolerància a fallades flexible, robusta, resilient i a baix cost. El treball ací presentat se centra en el nivell de hardware, amb la consideració de fons d'una potencial integració en una estratègia holística. Els esforços d'esta tesi s'han centrat en els següents aspectes: (i) la introducció de models de fallada addicionals requerits per a la representació adequada d'efectes físics que apareixen en les tecnologies de fabricació actuals, (ii) la provisió de ferramentes i mètodes per a la injecció eficient del models proposats i dels clàssics, (iii) l'anàlisi del mètode òptim per estudiar la robustesa de sistemes mitjançant l'ús d'injecció de fallades extensiva, i la posterior correlació amb capes de més alt nivell en un esforç per retallar el temps i cost de desenvolupament, (iv) la provisió de nous mètodes de detecció per cobrir els reptes plantejats pels models de fallades proposats, (v) la proposta d'estratègies de mitigació enfocades cap al tractament dels esmentats escenaris d'amenaça i (vi) la introducció d'una metodologia automatitzada de desplegament de diversos mecanismes de tolerància a fallades de forma robusta i sistemàtica. Els resultats de la present tesi constitueixen un conjunt de ferramentes i mètodes per ajudar el dissenyador de sistemes crítics en la seua tasca de desenvolupament de dissenys robustos, validats i a temps adaptats a la seua aplicació.Espinosa García, J. (2016). New Fault Detection, Mitigation and Injection Strategies for Current and Forthcoming Challenges of HW Embedded Designs [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/73146TESISCompendi