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

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Protecting GPU's Microarchitectural Vulnerabilities via Effective Selective Hardening

Graphics Processing Units (GPUs) are today adopted in several domains for which reliability is fundamental, such as self-driving cars and autonomous machines. Unfortunately, on one side GPUs have been shown to have a high error rate and, on the other side, the constraints imposed by real-time safety-critical applications make traditional, costly, replication-based hardening solutions inadequate. This paper proposes an effective microarchitectural selective hardening of GPU modules to mitigate those faults that affect instructions correct execution. We first characterize, through Register-Transfer Level (RTL) fault injections, the architectural vulnerabilities of a GPU model (FlexGripPlus). We specifically target transient faults in the functional units and pipeline registers of a GPU core. Then, we apply selective hardening by triplicating the locations in each module that we found to be more critical. The results show that selective hardening using Triple Modular Redundancy (TMR) can correct 85% to 99% of faults in the pipeline registers and from 50% to 100% of faults in the functional units. The proposed selective TMR strategy reduces the hardware overhead by up to 65% when compared with traditional TMR

Understanding the intraspecies genetic and phenotypic diversity of the clover symbiont Rhizobium leguminosarum

Rhizobia are agriculturally important bacteria capable of forming symbiosis with legumes and fixing atmospheric nitrogen which sustainably improves plant productivity and soil fertility. The Rhizobium leguminosarum species complex is highly genetically diverse and contains five genetically distinct genospecies. Significant phenotypic diversity is also displayed within Rhizobium leguminosarum; however, no phenotypes are genospecies-exclusive. The importance of the broad genetic diversity of Rhizobium leguminosarum and its influence on phenotypic diversity and rhizosphere-associated interactions are unclear. In this thesis, Rhizobium leguminosarum symbiovar trifolii (Rlt) intraspecies diversity was investigated by assessing the genetic and phenotypic variation of white clover nodule Rlt from agricultural field managements across Europe. This thesis identified that the significant genetic diversity of Rlt can manifest in substantial transcriptional and phenotypic variation across strains, and this diversity can influence plant-mediated symbiont selectivity and competitive strain interactions. A novel multiplexed high-throughput amplicon sequencing approach, MAUI-seq, was developed to improve detection of chimeras and other erroneous sequences for confident determination of intraspecies diversity from environmental samples. Using this method, significant Rlt nodule population diversity was identified between clover genotypes due to the combined effects of plant-host filtering and geospatial variation in allele frequencies of individual genes. Investigation of multiple Rlt strain transcriptomes demonstrated that genospecies displayed differences in core genome expression which was associated with phenotypic growth traits and putative differences in bacterial metabolism. Genomic and transcriptomic variation was utilised to identify transcriptional units conserved across strains. Pairwise growth competition experiments between Rlt strains further showed that significant competitive variation is evident and potentially associated with genospecies differences. This research demonstrates that utilising multiple strains can aid identification of species-specific traits by considering the representative variation within a species. The work presented here has laid the groundwork for future investigation into the implications of intraspecies diversity for symbiotic effectiveness in the rhizobia-legume symbiosis

Network-on-Chip

Addresses the Challenges Associated with System-on-Chip Integration Network-on-Chip: The Next Generation of System-on-Chip Integration examines the current issues restricting chip-on-chip communication efficiency, and explores Network-on-chip (NoC), a promising alternative that equips designers with the capability to produce a scalable, reusable, and high-performance communication backbone by allowing for the integration of a large number of cores on a single system-on-chip (SoC). This book provides a basic overview of topics associated with NoC-based design: communication infrastructure design, communication methodology, evaluation framework, and mapping of applications onto NoC. It details the design and evaluation of different proposed NoC structures, low-power techniques, signal integrity and reliability issues, application mapping, testing, and future trends. Utilizing examples of chips that have been implemented in industry and academia, this text presents the full architectural design of components verified through implementation in industrial CAD tools. It describes NoC research and developments, incorporates theoretical proofs strengthening the analysis procedures, and includes algorithms used in NoC design and synthesis. In addition, it considers other upcoming NoC issues, such as low-power NoC design, signal integrity issues, NoC testing, reconfiguration, synthesis, and 3-D NoC design. This text comprises 12 chapters and covers: The evolution of NoC from SoC—its research and developmental challenges NoC protocols, elaborating flow control, available network topologies, routing mechanisms, fault tolerance, quality-of-service support, and the design of network interfaces The router design strategies followed in NoCs The evaluation mechanism of NoC architectures The application mapping strategies followed in NoCs Low-power design techniques specifically followed in NoCs The signal integrity and reliability issues of NoC The details of NoC testing strategies reported so far The problem of synthesizing application-specific NoCs Reconfigurable NoC design issues Direction of future research and development in the field of NoC Network-on-Chip: The Next Generation of System-on-Chip Integration covers the basic topics, technology, and future trends relevant to NoC-based design, and can be used by engineers, students, and researchers and other industry professionals interested in computer architecture, embedded systems, and parallel/distributed systems

Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing

Zero-maintenance of electronic systems: Perspectives, challenges, and opportunities

Self-engineering systems that are capable of repairing themselves in-situ without the need for human decision (or intervention) could be used to achieve zero-maintenance. This philosophy is synonymous to the way in which the human body heals and repairs itself up to a point. This article synthesises issues related to an emerging area of self-healing technologies that links software and hardware mitigations strategies. Efforts are concentrated on built-in detection, masking and active mitigation that comprises self-recovery or self-repair capability, and has a focus on system resilience and recovering from fault events. Design techniques are critically reviewed to clarify the role of fault coverage, resource allocation and fault awareness, set in the context of existing and emerging printable/nanoscale manufacturing processes. The qualitative analysis presents new opportunities to form a view on the research required for a successful integration of zero-maintenance. Finally, the potential cost benefits and future trends are enumerated

Ultra Reliable Computing Systems

For high security and safety applications as well as general purpose applications, it is necessary to have ultra reliable computing systems. This dissertation describes our system of self-testable and self-repairable digital devices, especially, EPLDs (Electrically Programmable Logic Devices). In addition to significantly improving the reliability of digital systems, our self-healing and re-configurable system design with added repair capability can also provide higher yields, lower testing costs, and faster time-to-market for the semiconductor industry. The digital system in our approach is composed of blocks, which realize combinational and sequential circuits using GALs (Generic Array Logic Devices). We describe three techniques for fault-locating and fault-repairing in these devices. The methodology we used for evaluation of these methods and a comparison with devices that have no self-repair capability was simulation of the self-repair algorithms. Our simulations show that the lifetime for a GAL-based EPLD that uses our multiple self-repairing methods is longer than the lifetime of a GAL-based EPLD that uses a single self-repair method or no self-repair method. Specifically, our work demonstrates that the lifetime of a GAL can be increased by adding extra columns in the AND array of a GAL and extra output ORs in a GAL. It also gives information on how many extra columns and extra ORs a GAL needs and which self-repairing method should be used to guarantee a given lifetime. Thus, we can estimate an ideal point, where the maximum reliability can be reached with the minimum cost

Runtime Management of Multiprocessor Systems for Fault Tolerance, Energy Efficiency and Load Balancing

Efficiency of modern multiprocessor systems is hurt by unpredictable events: aging causes permanent faults that disable components; application spawnings and terminations taking place at arbitrary times, affect energy proportionality, causing energy waste; load imbalances reduce resource utilization, penalizing performance. This thesis demonstrates how runtime management can mitigate the negative effects of unpredictable events, making decisions guided by a combination of static information known in advance and parameters that only become known at runtime. We propose techniques for three different objectives: graceful degradation of aging-prone systems; energy efficiency of heterogeneous adaptive systems; and load balancing by means of work stealing. Managing aging-prone systems for graceful efficiency degradation, is based on a high-level system description that encapsulates hardware reconfigurability and workload flexibility and allows to quantify system efficiency and use it as an objective function. Different custom heuristics, as well as simulated annealing and a genetic algorithm are proposed to optimize this objective function as a response to component failures. Custom heuristics are one to two orders of magnitude faster, provide better efficiency for the first 20% of system lifetime and are less than 13% worse than a genetic algorithm at the end of this lifetime. Custom heuristics occasionally fail to satisfy reconfiguration cost constraints. As all algorithms\u27 execution time scales well with respect to system size, a genetic algorithm can be used as backup in these cases. Managing heterogeneous multiprocessors capable of Dynamic Voltage and Frequency Scaling is based on a model that accurately predicts performance and power: performance is predicted by combining static, application-specific profiling information and dynamic, runtime performance monitoring data; power is predicted using the aforementioned performance estimations and a set of platform-specific, static parameters, determined only once and used for every application mix. Three runtime heuristics are proposed, that make use of this model to perform partial search of the configuration space, evaluating a small set of configurations and selecting the best one. When best-effort performance is adequate, the proposed approach achieves 3% higher energy efficiency compared to the powersave governor and 2x better compared to the interactive and ondemand governors. When individual applications\u27 performance requirements are considered, the proposed approach is able to satisfy them, giving away 18% of system\u27s energy efficiency compared to the powersave, which however misses the performance targets by 23%; at the same time, the proposed approach maintains an efficiency advantage of about 55% compared to the other governors, which also satisfy the requirements. Lastly, to improve load balancing of multiprocessors, a partial and approximate view of the current load distribution among system cores is proposed, which consists of lightweight data structures and is maintained by each core through cheap operations. A runtime algorithm is developed, using this view whenever a core becomes idle, to perform victim core selection for work stealing, also considering system topology and memory hierarchy. Among 12 diverse imbalanced workloads, the proposed approach achieves better performance than random, hierarchical and local stealing for six workloads. Furthermore, it is at most 8% slower among the other six workloads, while competing strategies incur a penalty of at least 89% on some workload