Revisiting Vulnerability Analysis in Modern Microprocessors

Abstract-The notion of Architectural Vulnerability Factor (AVF) has been extensively used to evaluate various aspects of design robustness. While AVF has been a very popular way of assessing element resiliency, its calculation requires rigorous and extremely time-consuming experiments. Furthermore, recent radiation studies in 90 nm and 65 nm technology nodes demonstrate that up to 55 percent of Single Event Upsets (SEUs) result in Multiple Bit Upsets (MBUs), and thus the Single Bit Flip (SBF) model employed in computing AVF needs to be reassessed. In this paper, we present a method for calculating the vulnerability of modern microprocessors -using Statistical Fault Injection (SFI)-several orders of magnitude faster than traditional SFI techniques, while also using more realistic fault models which reflect the existence of MBUs. Our method partitions the design into various hierarchical levels and systematically performs incremental fault injections to generate vulnerability estimates. The presented method has been applied on an Intel microprocessor and an Alpha 21264 design, accelerating fault injection by 15Â, on average, and reducing computational cost for investigating the effect of MBUs. Extensive experiments, focusing on the effect of MBUs in modern microprocessors, corroborate that the SBF model employed by current vulnerability estimation tools is not sufficient to accurately capture the increasing effect of MBUs in contemporary processes

Understanding Soft Errors in Uncore Components

The effects of soft errors in processor cores have been widely studied. However, little has been published about soft errors in uncore components, such as memory subsystem and I/O controllers, of a System-on-a-Chip (SoC). In this work, we study how soft errors in uncore components affect system-level behaviors. We have created a new mixed-mode simulation platform that combines simulators at two different levels of abstraction, and achieves 20,000x speedup over RTL-only simulation. Using this platform, we present the first study of the system-level impact of soft errors inside various uncore components of a large-scale, multi-core SoC using the industrial-grade, open-source OpenSPARC T2 SoC design. Our results show that soft errors in uncore components can significantly impact system-level reliability. We also demonstrate that uncore soft errors can create major challenges for traditional system-level checkpoint recovery techniques. To overcome such recovery challenges, we present a new replay recovery technique for uncore components belonging to the memory subsystem. For the L2 cache controller and the DRAM controller components of OpenSPARC T2, our new technique reduces the probability that an application run fails to produce correct results due to soft errors by more than 100x with 3.32% and 6.09% chip-level area and power impact, respectively.Comment: to be published in Proceedings of the 52nd Annual Design Automation Conferenc

Efficient fault-injection-based assessment of software-implemented hardware fault tolerance

With continuously shrinking semiconductor structure sizes and lower supply voltages, the per-device susceptibility to transient and permanent hardware faults is on the rise. A class of countermeasures with growing popularity is Software-Implemented Hardware Fault Tolerance (SIHFT), which avoids expensive hardware mechanisms and can be applied application-specifically. However, SIHFT can, against intuition, cause more harm than good, because its overhead in execution time and memory space also increases the figurative “attack surface” of the system – it turns out that application-specific configuration of SIHFT is in fact a necessity rather than just an advantage. Consequently, target programs need to be analyzed for particularly critical spots to harden. SIHFT-hardened programs need to be measured and compared throughout all development phases of the program to observe reliability improvements or deteriorations over time. Additionally, SIHFT implementations need to be tested. The contributions of this dissertation focus on Fault Injection (FI) as an assessment technique satisfying all these requirements – analysis, measurement and comparison, and test. I describe the design and implementation of an FI tool, named Fail*, that overcomes several shortcomings in the state of the art, and enables research on the general drawbacks of simulation-based FI. As demonstrated in four case studies in the context of SIHFT research, Fail* provides novel fine-grained analysis techniques that exploit the newly gained possibility to analyze FI results from complete fault-space exploration. These analysis techniques aid SIHFT design decisions on the level of program modules, functions, variables, source-code lines, or single machine instructions. Based on the experience from the case studies, I address the problem of large computation efforts that accompany exhaustive fault-space exploration from two different angles: Firstly, I develop a heuristical fault-space pruning technique that allows to freely trade the total FI-experiment count for result accuracy, while still providing information on all possible faultspace coordinates. Secondly, I speed up individual TAP-based FI experiments by improving the fast-forwarding operation by several orders of magnitude for most workloads. Finally, I dissect current practices in FI-based evaluation of SIHFT-hardened programs, identify three widespread pitfalls in the result interpretation, and advance the state of the art by defining a novel comparison metric

High-fidelity error injection and acceleration techniques

As technology scales down, the likelihood of hardware errors that silently corrupt the results of applications is increasing. Evaluating the resilience of applications against hardware errors is thus of significant concern. Current evaluation techniques via error injection are either low-fidelity or inefficient in terms of using computing resources. This dissertation demonstrates that sophisticated integration of injectors across abstraction layers and novel sampling algorithms can significantly improve both the fidelity and efficiency. Specifically, this dissertation describes an open-source instruction-level error injector that generates high-fidelity hardware errors due to particle strikes and voltage droops. Two acceleration techniques, nested Monte Carlo and Injection-Point Overprovisioning, are proposed to speed up error injection campaigns by 1−2 orders of magnitude. This dissertation also answers the question of when high-fidelity is needed to evaluate the impact of hardware errors on applications and the effectiveness of error detectors.Electrical and Computer Engineerin

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Dependable Computing on Inexact Hardware through Anomaly Detection.

Reliability of transistors is on the decline as transistors continue to shrink in size. Aggressive voltage scaling is making the problem even worse. Scaled-down transistors are more susceptible to transient faults as well as permanent in-field hardware failures. In order to continue to reap the benefits of technology scaling, it has become imperative to tackle the challenges risen due to the decreasing reliability of devices for the mainstream commodity market. Along with the worsening reliability, achieving energy efficiency and performance improvement by scaling is increasingly providing diminishing marginal returns. More than any other time in history, the semiconductor industry faces the crossroad of unreliability and the need to improve energy efficiency. These challenges of technology scaling can be tackled by categorizing the target applications in the following two categories: traditional applications that have relatively strict correctness requirement on outputs and emerging class of soft applications, from various domains such as multimedia, machine learning, and computer vision, that are inherently inaccuracy tolerant to a certain degree. Traditional applications can be protected against hardware failures by low-cost detection and protection methods while soft applications can trade off quality of outputs to achieve better performance or energy efficiency. For traditional applications, I propose an efficient, software-only application analysis and transformation solution to detect data and control flow transient faults. The intelligence of the data flow solution lies in the use of dynamic application information such as control flow, memory and value profiling. The control flow protection technique achieves its efficiency by simplifying signature calculations in each basic block and by performing checking at a coarse-grain level. For soft applications, I develop a quality control technique. The quality control technique employs continuous, light-weight checkers to ensure that the approximation is controlled and application output is acceptable. Overall, I show that the use of low-cost checkers to produce dependable results on commodity systems---constructed from inexact hardware components---is efficient and practical.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113341/1/dskhudia_1.pd

Runtime Monitoring for Dependable Hardware Design

Mit dem Voranschreiten der Technologieskalierung und der Globalisierung der Produktion von integrierten Schaltkreisen eröffnen sich eine Fülle von Schwachstellen bezüglich der Verlässlichkeit von Computerhardware. Jeder Mikrochip wird aufgrund von Produktionsschwankungen mit einem einzigartigen Charakter geboren, welcher sich durch seine Arbeitsbedingungen, Belastung und Umgebung in individueller Weise entwickelt. Daher sind deterministische Modelle, welche zur Entwurfszeit die Verlässlichkeit prognostizieren, nicht mehr ausreichend um Integrierte Schaltkreise mit Nanometertechnologie sinnvoll abbilden zu können. Der Bedarf einer Laufzeitanalyse des Zustandes steigt und mit ihm die notwendigen Maßnahmen zum Erhalt der Zuverlässigkeit. Transistoren sind anfällig für auslastungsbedingte Alterung, die die Laufzeit der Schaltung erhöht und mit ihr die Möglichkeit einer Fehlberechnung. Hinzu kommen spezielle Abläufe die das schnelle Altern des Chips befördern und somit seine zuverlässige Lebenszeit reduzieren. Zusätzlich können strahlungsbedingte Laufzeitfehler (Soft-Errors) des Chips abnormales Verhalten kritischer Systeme verursachen. Sowohl das Ausbreiten als auch das Maskieren dieser Fehler wiederum sind abhängig von der Arbeitslast des Systems. Fabrizierten Chips können ebenfalls vorsätzlich während der Produktion boshafte Schaltungen, sogenannte Hardwaretrojaner, hinzugefügt werden. Dies kompromittiert die Sicherheit des Chips. Da diese Art der Manipulation vor ihrer Aktivierung kaum zu erfassen ist, ist der Nachweis von Trojanern auf einem Chip direkt nach der Produktion extrem schwierig. Die Komplexität dieser Verlässlichkeitsprobleme machen ein einfaches Modellieren der Zuverlässigkeit und Gegenmaßnahmen ineffizient. Sie entsteht aufgrund verschiedener Quellen, eingeschlossen der Entwicklungsparameter (Technologie, Gerät, Schaltung und Architektur), der Herstellungsparameter, der Laufzeitauslastung und der Arbeitsumgebung. Dies motiviert das Erforschen von maschinellem Lernen und Laufzeitmethoden, welche potentiell mit dieser Komplexität arbeiten können. In dieser Arbeit stellen wir Lösungen vor, die in der Lage sind, eine verlässliche Ausführung von Computerhardware mit unterschiedlichem Laufzeitverhalten und Arbeitsbedingungen zu gewährleisten. Wir entwickelten Techniken des maschinellen Lernens um verschiedene Zuverlässigkeitseffekte zu modellieren, zu überwachen und auszugleichen. Verschiedene Lernmethoden werden genutzt, um günstige Überwachungspunkte zur Kontrolle der Arbeitsbelastung zu finden. Diese werden zusammen mit Zuverlässigkeitsmetriken, aufbauend auf Ausfallsicherheit und generellen Sicherheitsattributen, zum Erstellen von Vorhersagemodellen genutzt. Des Weiteren präsentieren wir eine kosten-optimierte Hardwaremonitorschaltung, welche die Überwachungspunkte zur Laufzeit auswertet. Im Gegensatz zum aktuellen Stand der Technik, welcher mikroarchitektonische Überwachungspunkte ausnutzt, evaluieren wir das Potential von Arbeitsbelastungscharakteristiken auf der Logikebene der zugrundeliegenden Hardware. Wir identifizieren verbesserte Features auf Logikebene um feingranulare Laufzeitüberwachung zu ermöglichen. Diese Logikanalyse wiederum hat verschiedene Stellschrauben um auf höhere Genauigkeit und niedrigeren Overhead zu optimieren. Wir untersuchten die Philosophie, Überwachungspunkte auf Logikebene mit Hilfe von Lernmethoden zu identifizieren und günstigen Monitore zu implementieren um eine adaptive Vorbeugung gegen statisches Altern, dynamisches Altern und strahlungsinduzierte Soft-Errors zu schaffen und zusätzlich die Aktivierung von Hardwaretrojanern zu erkennen. Diesbezüglich haben wir ein Vorhersagemodell entworfen, welches den Arbeitslasteinfluss auf alterungsbedingte Verschlechterungen des Chips mitverfolgt und dazu genutzt werden kann, dynamisch zur Laufzeit vorbeugende Techniken, wie Task-Mitigation, Spannungs- und Frequenzskalierung zu benutzen. Dieses Vorhersagemodell wurde in Software implementiert, welche verschiedene Arbeitslasten aufgrund ihrer Alterungswirkung einordnet. Um die Widerstandsfähigkeit gegenüber beschleunigter Alterung sicherzustellen, stellen wir eine Überwachungshardware vor, welche einen Teil der kritischen Flip-Flops beaufsichtigt, nach beschleunigter Alterung Ausschau hält und davor warnt, wenn ein zeitkritischer Pfad unter starker Alterungsbelastung steht. Wir geben die Implementierung einer Technik zum Reduzieren der durch das Ausführen spezifischer Subroutinen auftretenden Belastung von zeitkritischen Pfaden. Zusätzlich schlagen wir eine Technik zur Abschätzung von online Soft-Error-Schwachstellen von Speicherarrays und Logikkernen vor, welche auf der Überwachung einer kleinen Gruppe Flip-Flops des Entwurfs basiert. Des Weiteren haben wir eine Methode basierend auf Anomalieerkennung entwickelt, um Arbeitslastsignaturen von Hardwaretrojanern während deren Aktivierung zur Laufzeit zu erkennen und somit eine letzte Verteidigungslinie zu bilden. Basierend auf diesen Experimenten demonstriert diese Arbeit das Potential von fortgeschrittener Feature-Extraktion auf Logikebene und lernbasierter Vorhersage basierend auf Laufzeitdaten zur Verbesserung der Zuverlässigkeit von Harwareentwürfen

Fault-tolerant satellite computing with modern semiconductors

Miniaturized satellites enable a variety space missions which were in the past infeasible, impractical or uneconomical with traditionally-designed heavier spacecraft. Especially CubeSats can be launched and manufactured rapidly at low cost from commercial components, even in academic environments. However, due to their low reliability and brief lifetime, they are usually not considered suitable for life- and safety-critical services, complex multi-phased solar-system-exploration missions, and missions with a longer duration. Commercial electronics are key to satellite miniaturization, but also responsible for their low reliability: Until 2019, there existed no reliable or fault-tolerant computer architectures suitable for very small satellites. To overcome this deficit, a novel on-board-computer architecture is described in this thesis.Robustness is assured without resorting to radiation hardening, but through software measures implemented within a robust-by-design multiprocessor-system-on-chip. This fault-tolerant architecture is component-wise simple and can dynamically adapt to changing performance requirements throughout a mission. It can support graceful aging by exploiting FPGA-reconfiguration and mixed-criticality.  Experimentally, we achieve 1.94W power consumption at 300Mhz with a Xilinx Kintex Ultrascale+ proof-of-concept, which is well within the powerbudget range of current 2U CubeSats. To our knowledge, this is the first COTS-based, reproducible on-board-computer architecture that can offer strong fault coverage even for small CubeSats.European Space AgencyComputer Systems, Imagery and Medi