Cross-layer system reliability assessment framework for hardware faults

System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.Peer ReviewedPostprint (author's final draft

Cross-layer reliability evaluation, moving from the hardware architecture to the system level: A CLERECO EU project overview

Advanced computing systems realized in forthcoming technologies hold the promise of a significant increase of computational capabilities. However, the same path that is leading technologies toward these remarkable achievements is also making electronic devices increasingly unreliable. Developing new methods to evaluate the reliability of these systems in an early design stage has the potential to save costs, produce optimized designs and have a positive impact on the product time-to-market. CLERECO European FP7 research project addresses early reliability evaluation with a cross-layer approach across different computing disciplines, across computing system layers and across computing market segments. The fundamental objective of the project is to investigate in depth a methodology to assess system reliability early in the design cycle of the future systems of the emerging computing continuum. This paper presents a general overview of the CLERECO project focusing on the main tools and models that are being developed that could be of interest for the research community and engineering practice

Cross-Layer Early Reliability Evaluation for the Computing cOntinuum

Advanced multifunctional computing systems realized in forthcoming technologies hold the promise of a significant increase of the computational capability that will offer end-users ever improving services and functionalities (e.g., next generation mobile devices, cloud services, etc.). However, the same path that is leading technologies toward these remarkable achievements is also making electronic devices increasingly unreliable, posing a threat to our society that is depending on the ICT in every aspect of human activities. Reliability of electronic systems is therefore a key challenge for the whole ICT technology and must be guaranteed without penalizing or slowing down the characteristics of the final products. CLERECO EU FP7 (GA No. 611404) research project addresses early accurate reliability evaluation and efficient exploitation of reliability at different design phases, since these aspects are two of the most important and challenging tasks toward this goal

Soft Error Effects on Arm Microprocessors: Early Estimations versus Chip Measurements

Extensive research efforts are being carried out to evaluate and improve the reliability of computing devices either through beam experiments or simulation-based fault injection. Unfortunately, it is still largely unclear to which extend fault injection can provide an accurate error rate estimation at early stages and if beam experiments can be used to identify the weakest resources in a device. The importance and challenges associated with a timely, but yet realistic reliability evaluation grow with the increase of complexity in both the hardware domain, with the integration of different types of cores in an SoC (System-on-Chip), and the software domain, with the OS (operating system) required to take full advantage of the available resources. In this paper, we combine and analyze data gathered with extensive beam experiments (on the final physical CPU hardware) and microarchitectural fault injections (on early microarchitectural CPU models). We target a standalone Arm Cortex-A5 CPU and an Arm Cortex-A9 CPU integrated into an SoC and evaluate their reliability in bare-metal and Linux-based configurations. Combining experimental data that covers more than 18 million years of device time with the result of more than 176,000 injections we find that both the SoC integration and the presence of the OS increase the system DUEs (Detected Unrecoverable Errors) rate (for different reasons) but do not significantly impact the SDCs (Silent Data Corruptions) rate which is solely attributed to the CPU core. Our reliability analysis demonstrates that even considering SoC integration and OS inclusion, early, pre-silicon microarchitecture-level fault injection delivers accurate SDC rates estimations and lower bounds for the DUE rates

Soft-error resilient on-chip memory structures

Soft errors induced by energetic particle strikes in on-chip memory structures, such as L1 data/instruction caches and register files, have become an increasing challenge in designing new generation reliable microprocessors. Due to their transient/random nature, soft errors cannot be captured by traditional verification and testing process due to the irrelevancy to the correctness of the logic. This dissertation is thus focusing on the reliability characterization and cost-effective reliable design of on-chip memories against soft errors. Due to various performance, area/size, and energy constraints in various target systems, many existing unoptimized protection schemes on cache memories may eventually prove significantly inadequate and ineffective. This work develops new lifetime models for data and tag arrays residing in both the data and instruction caches. These models facilitate the characterization of cache vulnerability of the stored items at various lifetime phases. The design methodology is further exemplified by the proposed reliability schemes targeting at specific vulnerable phases. Benchmarking is carried out to showcase the effectiveness of these approaches. The tag array demands high reliability against soft errors while the data array is fully protected in on-chip caches, because of its crucial importance to the correctness of cache accesses. Exploiting the address locality of memory accesses, this work proposes a Tag Replication Buffer (TRB) to protect information integrity of the tag array in the data cache with low performance, energy and area overheads. To provide a comprehensive evaluation of the tag array reliability, this work also proposes a refined evaluation metric, detected-without-replica-TVF (DOR-TVF), which combines the TVF and access-with-replica (AWR) analysis. Based on the DOR-TVF analysis, a TRB scheme with early write-back (TRB-EWB) is proposed, which achieves a zero DOR-TVF at a negligible performance overhead. Recent research, as well as the proposed optimization schemes in this cache vulnerability study, have focused on the design of cost-effective reliable data caches in terms of performance, energy, and area overheads based on the assumption of fixed error rates. However, for systems in operating environments that vary with time or location, those schemes will be either insufficient or over-designed for the changing error rates. This work explores the design of a self-adaptive reliable data cache that dynamically adapts its employed reliability schemes to the changing operating environments in order to maintain a target reliability. The experimental evaluation shows that the self-adaptive data cache achieves similar reliability to a cache protected by the most reliable scheme, while simultaneously minimizing the performance and power overheads. Besides the data/instruction caches, protecting the register file and its data buses is crucial to reliable computing in high-performance microprocessors. Since the register file is in the critical path of the processor pipeline, any reliable design that increases either the pressure on the register file or the register file access latency is not desirable. This work proposes to exploit narrow-width register values, which represent the majority of generated values, for making the duplicates within the same register data item. A detailed architectural vulnerability factor (AVF) analysis shows that this in-register duplication (IRD) scheme significantly reduces the AVF in the register file compared to the conventional design. The experimental evaluation also shows that IRD provides superior read-with-duplicate (RWD) and error detection/recovery rates under heavy error injection as compared to previous reliability schemes, while only incurring a small power overhead. By integrating the proposed reliable designs in data/instruction caches and register files, the vulnerability of the entire microprocessor is dramatically reduced. The new lifetime model, the self-adaptive design and the narrow-width value duplication scheme proposed in this work can also provide guidance to architects toward highly efficient reliable system design

A soft error mitigation scheme to increase the resilience of register file

Abstract Register files are essential and integral part of any microprocessor architecture. Soft errors in the register file can quickly spread to various parts of the system and result in silent data corruption. Traditional redundancy based schemes to protect the register file are prohibitive because register file is often in the timing critical path of the processor. Since it is one of the hottest blocks on the chip, adding any extra circuitry to it is not desirable. For embedded systems under severe cost constraints, where power, performance, area and reliability cannot be simply compromised, we propose a soft error reduction technique for register files. This thesis introduces a soft error mitigation scheme, called Self-Immunity to increase the resilience of the register file from soft errors, by using unused bits of the register file. It is desirable for processors that demand high register file integrity under stringent constraints. This thesis explains the implementation of our proposed technique to protect the register file from soft errors. And show the best overall results compared to state-of-the-art in register file vulnerability reduction with minimum impact on the area and power

SyRA: early system reliability analysis for cross-layer soft errors resilience in memory arrays of microprocessor systems

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cross-layer reliability is becoming the preferred solution when reliability is a concern in the design of a microprocessor-based system. Nevertheless, deciding how to distribute the error management across the different layers of the system is a very complex task that requires the support of dedicated frameworks for cross-layer reliability analysis. This paper proposes SyRA, a system-level cross-layer early reliability analysis framework for radiation induced soft errors in memory arrays of microprocessor-based systems. The framework exploits a multi-level hybrid Bayesian model to describe the target system and takes advantage of Bayesian inference to estimate different reliability metrics. SyRA implements several mechanisms and features to deal with the complexity of realistic models and implements a complete tool-chain that scales efficiently with the complexity of the system. The simulation time is significantly lower than micro-architecture level or RTL fault-injection experiments with an accuracy high enough to take effective design decisions. To demonstrate the capability of SyRA, we analyzed the reliability of a set of microprocessor-based systems characterized by different microprocessor architectures (i.e., Intel x86, ARM Cortex-A15, ARM Cortex-A9) running both the Linux operating system or bare metal. Each system under analysis executes different software workloads both from benchmark suites and from real applications.Peer ReviewedPostprint (author's final draft

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Towards Accurate Estimation of Error Sensitivity in Computer Systems

Fault injection is an increasingly important method for assessing, measuringand observing the system-level impact of hardware and software faults in computer systems. This thesis presents the results of a series of experimental studies in which fault injection was used to investigate the impact of bit-flip errors on program execution. The studies were motivated by the fact that transient hardware faults in microprocessors can cause bit-flip errors that can propagate to the microprocessors instruction set architecture registers and main memory. As the rate of such hardware faults is expected to increase with technology scaling, there is a need to better understand how these errors (known as ‘soft errors’) influence program execution, especially in safety-critical systems.Using ISA-level fault injection, we investigate how five aspects, or factors, influence the error sensitivity of a program. We define error sensitivity as the conditional probability that a bit-flip error in live data in an ISA-register or main-memory word will cause a program to produce silent data corruption (SDC; i.e., an erroneous result). We also consider the estimation of a measure called SDC count, which represents the number of ISA-level bit flips that cause an SDC.The five factors addressed are (a) the inputs processed by a program, (b) the level of compiler optimization, (c) the implementation of the program in the source code, (d) the fault model (single bit flips vs double bit flips) and (e)the fault-injection technique (inject-on-write vs inject-on-read). Our results show that these factors affect the error sensitivity in many ways; some factors strongly impact the error sensitivity or SDC count whereas others show a weaker impact. For example, our experiments show that single bit flips tend to cause SDCs more than double bit flips; compiler optimization positively impacts the SDC count but not necessarily the error sensitivity; the error sensitivity varies between 20% and 50% among the programs we tested; and variations in input affect the error sensitivity significantly for most of the tested programs