Implementing the weakest failure detector for solving consensus

The concept of unreliable failure detector was introduced by Chandra and Toueg as a mechanism that provides information about process failures. This mechanism has been used to solve several agreement problems, such as the consensus problem. In this paper, algorithms that implement failure detectors in partially synchronous systems are presented. First two simple algorithms of the weakest class to solve the consensus problem, namely the Eventually Strong class (⋄S), are presented. While the first algorithm is wait-free, the second algorithm is f-resilient, where f is a known upper bound on the number of faulty processes. Both algorithms guarantee that, eventually, all the correct processes agree permanently on a common correct process, i.e. they also implement a failure detector of the class Omega (Ω). They are also shown to be optimal in terms of the number of communication links used forever. Additionally, a wait-free algorithm that implements a failure detector of the Eventually Perfect class (⋄P) is presented. This algorithm is shown to be optimal in terms of the number of bidirectional links used forever

Compiling experience into knowledge

Typical application fields of Knowledge Based Systems are usually characterized by having human expertise as the only one source to specify their desired behavior. Therefore, their design, evaluation and refinement has to make effective use of this valuable source. After an introduction to the concept of collecting validation experience in a Validation Knowledge Base (VKB), the paper introduces an estimation of the significance of the cases collected in the VKB. A high significance signalizes that a VKB should not longer serve as a case-based source of external (outside the Knowledge Base) knowledge, but compiled into the Knowledge Base instead. Based on this significance estimation, a technology to compile well selected cases into the Knowledge Base of the system under evaluation is presented

Improving AI systems' dependability by utilizing historical knowledge

A Turing Test is a promising way to validate AI systems which usually have no way to proof correctness. However, human experts (validators) are often too busy to participate in it and sometimes have different opinions per person as well as per validation session. To cope with these and increase the validation dependability, a Validation Knowledge Base (VKB) in Turing Test - like validation is proposed. The VKB is constructed and maintained across various validation sessions. Primary benefits are (1) decreasing validators' workload, (2) refining the methodology itself, e.g. selecting dependable validators using V KB, and (3) increasing AI systems' dependabilities through dependable validation, e.g. support to identify optimal solutions. Finally, Validation Experts Software Agents (VESA) are introduced to further break limitations of human validator's dependability. Each VESA is a software agent corresponding to a particular human validator. This suggests the ability to systematically "construct" human-like validators by keeping personal validation knowledge per corresponding validator. This will bring a new dimension towards dependable AI systems

Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM

[EN] Due to transistor shrinking, intermittent faults are a major concern in current digital systems. This work presents an adaptive fault tolerance mechanism based on error correction codes (ECC), able to modify its behavior when the error conditions change without increasing the redundancy. As a case example, we have designed a mechanism that can detect intermittent faults and swap from an initial generic ECC to a specific ECC capable of tolerating one intermittent fault. We have inserted the mechanism in the memory system of a 32-bit RISC processor and validated it by using VHDL simulation-based fault injection. We have used two (39, 32) codes: a single error correction-double error detection (SEC-DED) and a code developed by our research group, called EPB3932, capable of correcting single errors and double and triple adjacent errors that include a bit previously tagged as error-prone. The results of injecting transient, intermittent, and combinations of intermittent and transient faults show that the proposed mechanism works properly. As an example, the percentage of failures and latent errors is 0% when injecting a triple adjacent fault after an intermittent stuck-at fault. We have synthesized the adaptive fault tolerance mechanism proposed in two types of FPGAs: non-reconfigurable and partially reconfigurable. In both cases, the overhead introduced is affordable in terms of hardware, time and power consumption.This research was supported in part by the Spanish Government, project TIN2016-81,075-R, and by Primeros Proyectos de Investigacion (PAID-06-18), Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV), project 20190032.Baraza Calvo, JC.; Gracia-Morán, J.; Saiz-Adalid, L.; Gil Tomás, DA.; Gil, P. (2020). Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM. Electronics. 9(12):1-30. https://doi.org/10.3390/electronics9122074S130912International Technology Roadmap for Semiconductors (ITRS)http://www.itrs2.net/2013-itrs.htmlJeng, S.-L., Lu, J.-C., & Wang, K. (2007). A Review of Reliability Research on Nanotechnology. IEEE Transactions on Reliability, 56(3), 401-410. doi:10.1109/tr.2007.903188Ibe, E., Taniguchi, H., Yahagi, Y., Shimbo, K., & Toba, T. (2010). Impact of Scaling on Neutron-Induced Soft Error in SRAMs From a 250 nm to a 22 nm Design Rule. IEEE Transactions on Electron Devices, 57(7), 1527-1538. doi:10.1109/ted.2010.2047907Boussif, A., Ghazel, M., & Basilio, J. C. (2020). Intermittent fault diagnosability of discrete event systems: an overview of automaton-based approaches. Discrete Event Dynamic Systems, 31(1), 59-102. doi:10.1007/s10626-020-00324-yConstantinescu, C. (2003). Trends and challenges in VLSI circuit reliability. IEEE Micro, 23(4), 14-19. doi:10.1109/mm.2003.1225959Bondavalli, A., Chiaradonna, S., Di Giandomenico, F., & Grandoni, F. (2000). Threshold-based mechanisms to discriminate transient from intermittent faults. IEEE Transactions on Computers, 49(3), 230-245. doi:10.1109/12.841127Contant, O., Lafortune, S., & Teneketzis, D. (2004). Diagnosis of Intermittent Faults. Discrete Event Dynamic Systems, 14(2), 171-202. doi:10.1023/b:disc.0000018570.20941.d2Sorensen, B. A., Kelly, G., Sajecki, A., & Sorensen, P. W. (s. f.). An analyzer for detecting intermittent faults in electronic devices. Proceedings of AUTOTESTCON ’94. doi:10.1109/autest.1994.381590Gracia-Moran, J., Gil-Tomas, D., Saiz-Adalid, L. J., Baraza, J. C., & Gil-Vicente, P. J. (2010). Experimental validation of a fault tolerant microcomputer system against intermittent faults. 2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN). doi:10.1109/dsn.2010.5544288Fujiwara, E. (2005). Code Design for Dependable Systems. doi:10.1002/0471792748Hamming, R. W. (1950). Error Detecting and Error Correcting Codes. Bell System Technical Journal, 29(2), 147-160. doi:10.1002/j.1538-7305.1950.tb00463.xSaiz-Adalid, L.-J., Gil-Vicente, P.-J., Ruiz-García, J.-C., Gil-Tomás, D., Baraza, J.-C., & Gracia-Morán, J. (2013). Flexible Unequal Error Control Codes with Selectable Error Detection and Correction Levels. Computer Safety, Reliability, and Security, 178-189. doi:10.1007/978-3-642-40793-2_17Frei, R., McWilliam, R., Derrick, B., Purvis, A., Tiwari, A., & Di Marzo Serugendo, G. (2013). Self-healing and self-repairing technologies. The International Journal of Advanced Manufacturing Technology, 69(5-8), 1033-1061. doi:10.1007/s00170-013-5070-2Maiz, J., Hareland, S., Zhang, K., & Armstrong, P. (s. f.). Characterization of multi-bit soft error events in advanced SRAMs. IEEE International Electron Devices Meeting 2003. doi:10.1109/iedm.2003.1269335Schroeder, B., Pinheiro, E., & Weber, W.-D. (2011). DRAM errors in the wild. Communications of the ACM, 54(2), 100-107. doi:10.1145/1897816.1897844BanaiyanMofrad, A., Ebrahimi, M., Oboril, F., Tahoori, M. B., & Dutt, N. (2015). Protecting caches against multi-bit errors using embedded erasure coding. 2015 20th IEEE European Test Symposium (ETS). doi:10.1109/ets.2015.7138735Kim, J., Sullivan, M., Lym, S., & Erez, M. (2016). All-Inclusive ECC: Thorough End-to-End Protection for Reliable Computer Memory. 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA). doi:10.1109/isca.2016.60Hwang, A. A., Stefanovici, I. A., & Schroeder, B. (2012). Cosmic rays don’t strike twice. ACM SIGPLAN Notices, 47(4), 111-122. doi:10.1145/2248487.2150989Gil-Tomás, D., Gracia-Morán, J., Baraza-Calvo, J.-C., Saiz-Adalid, L.-J., & Gil-Vicente, P.-J. (2012). Studying the effects of intermittent faults on a microcontroller. Microelectronics Reliability, 52(11), 2837-2846. doi:10.1016/j.microrel.2012.06.004Plasma CPU Modelhttps://opencores.org/projects/plasmaArlat, J., Aguera, M., Amat, L., Crouzet, Y., Fabre, J.-C., Laprie, J.-C., … Powell, D. (1990). Fault injection for dependability validation: a methodology and some applications. IEEE Transactions on Software Engineering, 16(2), 166-182. doi:10.1109/32.44380Gil-Tomas, D., Gracia-Moran, J., Baraza-Calvo, J.-C., Saiz-Adalid, L.-J., & Gil-Vicente, P.-J. (2012). Analyzing the Impact of Intermittent Faults on Microprocessors Applying Fault Injection. IEEE Design & Test of Computers, 29(6), 66-73. doi:10.1109/mdt.2011.2179514Rashid, L., Pattabiraman, K., & Gopalakrishnan, S. (2010). Modeling the Propagation of Intermittent Hardware Faults in Programs. 2010 IEEE 16th Pacific Rim International Symposium on Dependable Computing. doi:10.1109/prdc.2010.52Amiri, M., Siddiqui, F. M., Kelly, C., Woods, R., Rafferty, K., & Bardak, B. (2016). FPGA-Based Soft-Core Processors for Image Processing Applications. Journal of Signal Processing Systems, 87(1), 139-156. doi:10.1007/s11265-016-1185-7Hailesellasie, M., Hasan, S. R., & Mohamed, O. A. (2019). MulMapper: Towards an Automated FPGA-Based CNN Processor Generator Based on a Dynamic Design Space Exploration. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). doi:10.1109/iscas.2019.8702589Mittal, S. (2018). A survey of FPGA-based accelerators for convolutional neural networks. Neural Computing and Applications, 32(4), 1109-1139. doi:10.1007/s00521-018-3761-1Intel Completes Acquisition of Alterahttps://newsroom.intel.com/news-releases/intel-completes-acquisition-of-altera/#gs.mi6ujuAMD to Acquire Xilinx, Creating the Industry’s High Performance Computing Leaderhttps://www.amd.com/en/press-releases/2020-10-27-amd-to-acquire-xilinx-creating-the-industry-s-high-performance-computingKim, K. H., & Lawrence, T. F. (s. f.). Adaptive fault tolerance: issues and approaches. [1990] Proceedings. Second IEEE Workshop on Future Trends of Distributed Computing Systems. doi:10.1109/ftdcs.1990.138292Gonzalez, O., Shrikumar, H., Stankovic, J. A., & Ramamritham, K. (s. f.). Adaptive fault tolerance and graceful degradation under dynamic hard real-time scheduling. Proceedings Real-Time Systems Symposium. doi:10.1109/real.1997.641271Jacobs, A., George, A. D., & Cieslewski, G. (2009). Reconfigurable fault tolerance: A framework for environmentally adaptive fault mitigation in space. 2009 International Conference on Field Programmable Logic and Applications. doi:10.1109/fpl.2009.5272313Shin, D., Park, J., Park, J., Paul, S., & Bhunia, S. (2017). Adaptive ECC for Tailored Protection of Nanoscale Memory. IEEE Design & Test, 34(6), 84-93. doi:10.1109/mdat.2016.2615844Silva, F., Muniz, A., Silveira, J., & Marcon, C. (2020). CLC-A: An Adaptive Implementation of the Column Line Code (CLC) ECC. 2020 33rd Symposium on Integrated Circuits and Systems Design (SBCCI). doi:10.1109/sbcci50935.2020.9189901Mukherjee, S. S., Emer, J., Fossum, T., & Reinhardt, S. K. (s. f.). Cache scrubbing in microprocessors: myth or necessity? 10th IEEE Pacific Rim International Symposium on Dependable Computing, 2004. Proceedings. doi:10.1109/prdc.2004.1276550Saleh, A. M., Serrano, J. J., & Patel, J. H. (1990). Reliability of scrubbing recovery-techniques for memory systems. IEEE Transactions on Reliability, 39(1), 114-122. doi:10.1109/24.52622X9SRA User’s Manual (Rev. 1.1)https://www.manualshelf.com/manual/supermicro/x9sra/user-s-manual-1-1.htmlChishti, Z., Alameldeen, A. R., Wilkerson, C., Wu, W., & Lu, S.-L. (2009). Improving cache lifetime reliability at ultra-low voltages. Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture - Micro-42. doi:10.1145/1669112.1669126Datta, R., & Touba, N. A. (2011). Designing a fast and adaptive error correction scheme for increasing the lifetime of phase change memories. 29th VLSI Test Symposium. doi:10.1109/vts.2011.5783773Kim, J., Lim, J., Cho, W., Shin, K.-S., Kim, H., & Lee, H.-J. (2016). Adaptive Memory Controller for High-performance Multi-channel Memory. JSTS:Journal of Semiconductor Technology and Science, 16(6), 808-816. doi:10.5573/jsts.2016.16.6.808Yuan, L., Liu, H., Jia, P., & Yang, Y. (2015). Reliability-Based ECC System for Adaptive Protection of NAND Flash Memories. 2015 Fifth International Conference on Communication Systems and Network Technologies. doi:10.1109/csnt.2015.23Zhou, Y., Wu, F., Lu, Z., He, X., Huang, P., & Xie, C. (2019). SCORE. ACM Transactions on Architecture and Code Optimization, 15(4), 1-25. doi:10.1145/3291052Lu, S.-K., Li, H.-P., & Miyase, K. (2018). Adaptive ECC Techniques for Reliability and Yield Enhancement of Phase Change Memory. 2018 IEEE 24th International Symposium on On-Line Testing And Robust System Design (IOLTS). doi:10.1109/iolts.2018.8474118Chen, J., Andjelkovic, M., Simevski, A., Li, Y., Skoncej, P., & Krstic, M. (2019). Design of SRAM-Based Low-Cost SEU Monitor for Self-Adaptive Multiprocessing Systems. 2019 22nd Euromicro Conference on Digital System Design (DSD). doi:10.1109/dsd.2019.00080Wang, X., Jiang, L., & Chakrabarty, K. (2020). LSTM-based Analysis of Temporally- and Spatially-Correlated Signatures for Intermittent Fault Detection. 2020 IEEE 38th VLSI Test Symposium (VTS). doi:10.1109/vts48691.2020.9107600Ebrahimi, H., & G. Kerkhoff, H. (2018). Intermittent Resistance Fault Detection at Board Level. 2018 IEEE 21st International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS). doi:10.1109/ddecs.2018.00031Ebrahimi, H., & Kerkhoff, H. G. (2020). A New Monitor Insertion Algorithm for Intermittent Fault Detection. 2020 IEEE European Test Symposium (ETS). doi:10.1109/ets48528.2020.9131563Hsiao, M. Y. (1970). A Class of Optimal Minimum Odd-weight-column SEC-DED Codes. IBM Journal of Research and Development, 14(4), 395-401. doi:10.1147/rd.144.0395Benso, A., & Prinetto, P. (Eds.). (2004). Fault Injection Techniques and Tools for Embedded Systems Reliability Evaluation. Frontiers in Electronic Testing. doi:10.1007/b105828Gracia, J., Saiz, L. J., Baraza, J. C., Gil, D., & Gil, P. J. (2008). Analysis of the influence of intermittent faults in a microcontroller. 2008 11th IEEE Workshop on Design and Diagnostics of Electronic Circuits and Systems. doi:10.1109/ddecs.2008.4538761ZC702 Evaluation Board for the Zynq-7000 XC7Z020 SoChttps://www.xilinx.com/support/documentation/boards_and_kits/zc702_zvik/ug850-zc702-eval-bd.pd

An extensive study on iterative solver resilience : characterization, detection and prediction

Soft errors caused by transient bit flips have the potential to significantly impactan applicalion's behavior. This has motivated the design of an array of techniques to detect, isolate, and correct soft errors using microarchitectural, architectural, compilation­based, or application-level techniques to minimize their impact on the executing application. The first step toward the design of good error detection/correction techniques involves an understanding of an application's vulnerability to soft errors. This work focuses on silent data e orruption's effects on iterative solvers and efforts to mitigate those effects. In this thesis, we first present the first comprehensive characterizalion of !he impact of soft errors on !he convergen ce characteris tics of six iterative methods using application-level fault injection. We analyze the impact of soft errors In terms of the type of error (single-vs multi-bit), the distribution and location of bits affected, the data structure and statement impacted, and varialion with time. We create a public access database with more than 1.5 million fault injection results. We then analyze the performance of soft error detection mechanisms and present the comparalive results. Molivated by our observations, we evaluate a machine-learning based detector that takes as features that are the runtime features observed by the individual detectors to arrive al their conclusions. Our evalualion demonstrates improved results over individual detectors. We then propase amachine learning based method to predict a program's error behavior to make fault injection studies more efficient. We demonstrate this method on asse ssing the performance of soft error detectors. We show that our method maintains 84% accuracy on average with up to 53% less cost. We also show, once a model is trained further fault injection tests would cost 10% of the expected full fault injection runs.“Soft errors” causados por cambios de estado transitorios en bits, tienen el potencial de impactar significativamente el comportamiento de una aplicación. Esto, ha motivado el diseño de una variedad de técnicas para detectar, aislar y corregir soft errors aplicadas a micro-arquitecturas, arquitecturas, tiempo de compilación y a nivel de aplicación para minimizar su impacto en la ejecución de una aplicación. El primer paso para diseñar una buna técnica de detección/corrección de errores, implica el conocimiento de las vulnerabilidades de la aplicación ante posibles soft errors. Este trabajo se centra en los efectos de la corrupción silenciosa de datos en soluciones iterativas, así como en los esfuerzos para mitigar esos efectos. En esta tesis, primeramente, presentamos la primera caracterización extensiva del impacto de soft errors sobre las características convergentes de seis métodos iterativos usando inyección de fallos a nivel de aplicación. Analizamos el impacto de los soft errors en términos del tipo de error (único vs múltiples-bits), de la distribución y posición de los bits afectados, las estructuras de datos, instrucciones afectadas y de las variaciones en el tiempo. Creamos una base de datos pública con más de 1.5 millones de resultados de inyección de fallos. Después, analizamos el desempeño de mecanismos de detección de soft errors actuales y presentamos los resultados de su comparación. Motivados por las observaciones de los resultados presentados, evaluamos un detector de soft errors basado en técnicas de machine learning que toma como entrada las características observadas en el tiempo de ejecución individual de los detectores anteriores al llegar a su conclusión. La evaluación de los resultados obtenidos muestra una mejora por sobre los detectores individualmente. Basados en estos resultados propusimos un método basado en machine learning para predecir el comportamiento de los errores en un programa con el fin de hacer el estudio de inyección de errores mas eficiente. Presentamos este método para evaluar el rendimiento de los detectores de soft errors. Demostramos que nuestro método mantiene una precisión del 84% en promedio con hasta un 53% de mejora en el tiempo de ejecución. También mostramos que una vez que un modelo ha sido entrenado, las pruebas de inyección de errores siguientes costarían 10% del tiempo esperado de ejecución.Postprint (published version

Resilience-Building Technologies: State of Knowledge -- ReSIST NoE Deliverable D12

This document is the first product of work package WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellenc