FIMSIM: A fault injection infrastructure for microarchitectural simulators

Fault injection is a widely used approach for experiment-based dependability evaluation in which faults can be injected to the hardware, to the simulator or to the software. Simulation based fault injection is more appealing for researchers, since it can be utilized at the early design stage of the processor. As such, it enables a preliminary analysis of the correlation between the criticality of circuit level faults and their impact on applications. However, the lack of publicly available fault injectors for microarchitecture level simulators brings extra burden of designing and implementing fault injectors to the researchers who evaluate microarchitecture dependability. In this study, we present FIMSIM, to the best of our knowledge, the first publicly available fault injection simulator at the microarchitecture level. FIMSIM is a compact tool which is capable of injecting transient, permanent, intermittent and multi-bit faults. Therefore, FIMSIM provides the opportunity to comprehensively evaluate the vulnerability of different microarchitectural structures against different fault models.Postprint (published version

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

Characterizing the Effects of Intermittent Faults on a Processor for Dependability Enhancement Strategy

As semiconductor technology scales into the nanometer regime, intermittent faults have become an increasing threat. This paper focuses on the effects of intermittent faults on NET versus REG on one hand and the implications for dependability strategy on the other. First, the vulnerability characteristics of representative units in OpenSPARC T2 are revealed, and in particular, the highly sensitive modules are identified. Second, an arch-level dependability enhancement strategy is proposed, showing that events such as core/strand running status and core-memory interface events can be candidates of detectable symptoms. A simple watchdog can be deployed to detect application running status (IEXE event). Then SDC (silent data corruption) rate is evaluated demonstrating its potential. Third and last, the effects of traditional protection schemes in the target CMT to intermittent faults are quantitatively studied on behalf of the contribution of each trap type, demonstrating the necessity of taking this factor into account for the strategy

Evaluation of methods for determining hardware projected life

An investigation of existing methods of predicting hardware life is summarized by reviewing programs having long life requirements, current research efforts on long life problems, and technical papers reporting work on life predicting techniques. The results indicate that there are no accurate quantitative means to predict hardware life for system level hardware. The effectiveness of test programs and the cause of hardware failures is considered

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

Error-efficient computing systems

This survey explores the theory and practice of techniques to make computing systems faster or more energy-efficient by allowing them to make controlled errors. In the same way that systems which only use as much energy as necessary are referred to as being energy-efficient, you can think of the class of systems addressed by this survey as being error-efficient: They only prevent as many errors as they need to. The definition of what constitutes an error varies across the parts of a system. And the errors which are acceptable depend on the application at hand. In computing systems, making errors, when behaving correctly would be too expensive, can conserve resources. The resources conserved may be time: By making some errors, systems may be faster. The resource may also be energy: A system may use less power from its batteries or from the electrical grid by only avoiding certain errors while tolerating benign errors that are associated with reduced power consumption. The resource in question may be an even more abstract quantity such as consistency of ordering of the outputs of a system. This survey is for anyone interested in an end-to-end view of one set of techniques that address the theory and practice of making computing systems more efficient by trading errors for improved efficiency