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ERROR-CORRECTING CODES OVER Z(2M) FOR algorithm-based fault-tolerance

IEEE TRANSACTIONS ON COMPUTERS 1994年第3期43卷 370-374页

作者： FENG, GL RAO, TRN KOLLURU, MS Center for Adv. Comput. Studies Southwestern Louisiana Univ. Lafayette LA USA

algorithm-based fault tolerance is a scheme of low-cost error protection in real-time digital signal processing environments and other computation-intensive tasks. In this paper, a new method for encoding data is proposed and, furthermore, tow kinds of error-correcting codes over Z2m, which can be used with fixed-point arithmetic in practical algorithm-based fault tolerant systems, are introduced.

关键词： algorithm-based fault tolerance BCH-LIKE CODES DECODING DATA ENCODING DATA ERROR-CORRECTING CODES OVER A RING REED SOLOMON-LIKE CODES

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FT-CNN: algorithm-based fault tolerance for Convolutional Neural Networks

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第7期32卷 1677-1689页

作者： Zhao, Kai Di, Sheng Li, Sihuan Liang, Xin Zhai, Yujia Chen, Jieyang Ouyang, Kaiming Cappello, Franck Chen, Zizhong Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this article, we focus on how to protect the CNN inference process against soft errors as efficiently as possible, with the following three contributions. (1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations. (2) We design a novel workflow integrating all the proposed schemes to obtain a high detection/correction ability with limited total runtime overhead. (3) We perform our evaluation using ImageNet with well-known CNN models including AlexNet, VGG-19, ResNet-18, and YOLOv2. Experimental results demonstrate that our implementation can handle soft errors with very limited runtime overhead (4%similar to 8% in both error-free and error-injected situations).

关键词： Convolution Runtime Kernel fault tolerant systems fault tolerance Error correction codes Mathematical model algorithm-based fault tolerance deep learning silent data corruption reliability high-performance computing

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Combinatorial analysis of check set construction for algorithm-based fault tolerance systems

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 1998年第3期12卷 255-260页

作者： Wang, DQ Zhao, LC Dalian Maritime Univ Dept Basic Sci Dalian 116026 Peoples R China

algorithm-based fault tolerance (ABFT) is a low-cost system-level concurrent error detection and fault location scheme. The design problem for an ABFT system is concerned with the construction of a check set for detecting errors or faults. In this paper, we analyze the construction of check sets from a combinatorial perspective, and propose a necessary and sufficient condition for the design of a check set that detects a given number of errors. We also propose a new bound for detecting three errors for algorithm-based fault tolerance systems.

关键词： algorithm-based fault tolerance check set combinatorial problem error detecting

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Generalized algorithm-based fault tolerance: Error correction via Kalman estimation

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IEEE TRANSACTIONS ON COMPUTERS 1998年第6期47卷 639-655页

作者： Redinbo, GR Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

An extension to algorithm-based fault tolerance (ABFT) methodologies shows how parity values dictated by a real convolutional code can be employed by Kalman estimation techniques to perform real number correction for protecting linear processing systems, intermittent failures appearing in the output samples are detected and corrected using only the syndromes normally generated in ABFT schemes. The algebraic structure of a real convolutional code provides separation needed by recursive Kalman state estimators to affect mean-square error correction. State and parity measurement equations model faults and computational noise in both the linear processing and parity generation subassemblies, and, in a departure from previous models, the noise sources are considered time-varying. The Kalman one-step estimator which makes decisions on all parity values up to the present point is determined, and it separates naturally into detection and correction operations permitting corrective action only when the detection levels exceed thresholds based on roundoff noise energy. The detector/corrector uses efficient multirate block processing techniques as determined by the real convolutional code. A smoothed fixed-lag Kalman estimator which uses parity values for a fixed amount beyond the point of interest is needed to complete the correction. It employs one-step estimator quantities and implementation simplifications are possible. Examples showing the correction behavior and mean-square error performance are presented, and the size of overhead calculations for detection and correction is estimated. A protected processing system is constructed by introducing additional subassemblies, mostly comparators, with the detection and correction parts already described. Under the usual assumptions of at most a single subassembly failure, no improperly detected or corrected data leave the overall protected configuration.

关键词： algorithm-based fault tolerance fault-tolerant linear processing Kalman recursive filtering mean-square error estimation real convolutional codes real number error correction time-varying fault models totally self-checking comparators

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PARTITIONED ENCODING-SCHEMES FOR algorithm-based fault-tolerance IN MASSIVELY-PARALLEL SYSTEMS

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 1994年第6期5卷 649-653页

作者： REXFORD, J JHA, NK PRINCETON UNIV DEPT ELECT ENGNPRINCETONNJ 08544

This short note considers the applicability of algorithm-based fault tolerance (ABFT) to massively parallel scientific computation. Existing ABFT schemes can provide effective fault tolerance at a low cost for computation on matrices of moderate size;however, the methods do not scale well to floating-point operations on large systems. This short note proposes the use of a partitioned linear encoding scheme to provide scalability. Matrix algorithms employing this scheme are presented and compared to current ABFT schemes. It is shown that the partitioned scheme provides scalable linear codes with improved numerical properties with only a small increase in hardware and time overhead.

关键词： algorithm-based fault tolerance CHECKSUM CODE ERROR DETECTION ERROR CORRECTION TRANSIENT ERRORS

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Mantissa-preserving operations and robust algorithm-based fault tolerance for matrix computations

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IEEE TRANSACTIONS ON COMPUTERS 1996年第4期45卷 408-424页

作者： Dutt, S Assaad, FT Dept. of Electr. Eng. Minnesota Univ. Minneapolis MN USA

A system-level method for achieving fault tolerance called algorithm-based fault tolerance (ABFT) has been proposed by a number of researchers. Many ABFT schemes use a floating-point checksum test to detect computation errors resulting from hardware faults. This makes the tests susceptible to roundoff inaccuracies in floating-point operations, which either cause false alarms or lead to undetected errors. Thresholding of the equality test has been commonly used to avoid false alarms;however, a good threshold that minimizes false alarms without reducing the error coverage significantly is difficult to find, especially when not much is known about the input data. Furthermore, thresholded checksums will inevitably miss lower-bit errors, which can get magnified as a computation such as LU decomposition progresses. Here we develop a theory for applying integer mantissa checksum tests to ''mantissa-preserving'' floating-point computations. This test is not susceptible to roundoff problems and yields 100% error coverage without false alarms. For computations that are not fully mantissa-preserving, we show how to apply the mantissa checksum test to the mantissa-preserving components of the computation and the floating-point test to the rest of the computation. We apply this general methodology to matrix-matrix multiplication and LU decomposition (using the Gaussian elimination (GE) algorithm), and find that the accuracy of this new ''hybrid'' testing scheme is substantially higher than the floating-point test with thresholding, and also that its time overhead with respect to the floating-point test is nominal (15% and 9.5% on the average for matrix multiplication and LU decomposition, respectively). The hybrid test can also be easily applied to other computations like matrix inversion that use the GE algorithm. We prove that the mantissa-based integer checksum test for both matrix multiplication and LU decomposition is able to detect at least three errors in the floating-poi

关键词： algorithm-based fault tolerance floating-point checksum test hybrid checksum test mantissa checksum test mantissa-preserving operations matrix multiplication LU decomposition roundoff errors thresholding

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Efficient techniques for the analysis of algorithm-based fault tolerance (ABFT) schemes

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IEEE TRANSACTIONS ON COMPUTERS 1996年第4期45卷 499-503页

作者： Nair, VSS Abraham, JA Banerjee, P UNIV TEXAS COMP ENGN RES CTRAUSTINTX 78712 UNIV ILLINOIS CTR RELIABLE & HIGH PERFORMANCE COMPURBANAIL 61801

This paper presents a model which can be used to characterize the diagnosability of algorithm-based fault Tolerant (ABFT) systems. In the model, the relationship between processors computing useful data, the output data, and the check processors is defined in terms of matrix entries. Necessary and sufficient conditions for detecting and locating faults in the processors are derived, and based on them, efficient algorithms to evaluate the fault detection and location capabilities oi the system are developed.

关键词： multiprocessor systems faults and errors system-level diagnosis algorithm-based fault tolerance modeling and analysis

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Parallel Reduction to Hessenberg Form with algorithm-based fault tolerance 13

Parallel Reduction to Hessenberg Form with Algorithm-Based F...

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International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Jia, Yulu Bosilca, George Dongarra, Jack J. Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)9781450323789

This paper studies the resilience of a two-sided factorization and presents a generic algorithm-based approach capable of making two-sided factorizations resilient. We establish the theoretical proof of the correctness and the numerical stability of the approach in the context of a Hessenberg Reduction (HR) and present the scalability and performance results of a practical implementation. Our method is a hybrid algorithm combining an algorithm based fault tolerance (ABFT) technique with diskless checkpointing to fully protect the data. We protect the trailing and the initial part of the matrix with checksums, and protect finished panels in the panel scope with diskless checkpoints. Compared with the original HR (the ScaLA-PACK PDGEHRD routine) our fault-tolerant algorithm introduces very little overhead, and maintains the same level of scalability. We prove that the overhead shows a decreasing trend as the size of the matrix or the size of the process grid increases.

关键词： algorithm-based fault tolerance Hessenberg reduction ScaLA-PACK Dense linear algebra Parallel numerical libraries

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Rethinking algorithm-based fault tolerance with a Cooperative Software-Hardware Approach 13

Rethinking Algorithm-Based Fault Tolerance with a Cooperativ...

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International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Li, Dong Chen, Zizhong Wu, Panruo Vetter, Jeffrey S. Oak Ridge Natl Lab Oak Ridge TN 37831 USA Univ Calif Riverside Riverside CA 92521 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450323789

algorithm-based fault tolerance (ABFT) is a highly efficient resilience solution for many widely-used scientific computing kernels. However, in the context of the resilience ecosystem, ABFT is completely opaque to any underlying hardware resilience mechanisms. As a result, some data structures are over-protected by ABFT and hardware, which leads to redundant costs in terms of performance and energy. In this paper, we rethink ABFT using an integrated view including both software and hardware with the goal of improving performance and energy efficiency of ABFT-enabled applications. In particular, we study how to coordinate ABFT and error-correcting code (ECC) for main memory, and investigate the impact of this coordination on performance, energy, and resilience for ABFT-enabled applications. Scaling tests and analysis indicate that our approach saves up to 25% for system energy (and up to 40% for dynamic memory energy) with up to 18% performance improvement over traditional approaches of ABFT with ECC.

关键词： algorithm-based fault tolerance error-correcting code adaptive resilience

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Numerical Defect Correction as an algorithm-based fault tolerance Technique for Iterative Solvers

Numerical Defect Correction as an Algorithm-Based Fault Tole...

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17th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC)

作者： Oboril, Fabian Tahoori, Mehdi B. Heuveline, Vincent Lukarski, Dimitar Weiss, Jan-Philipp Karlsruhe Inst Technol KIT Chair Dependable Nano Comp CDNC Karlsruhe Germany Karlsruhe Inst Technol KIT Engn Math & Comp Lab EMCL Karlsruhe Germany Karlsruhe Inst Technol KIT Shared Res Grp New Frontiers High Performance Comp Exploit Multicore & Coprocessor Technol Karlsruhe Germany

ISBN: (纸本)9780769545905

As hardware devices like processor cores and memory sub-systems based on nano-scale technology nodes become more unreliable, the need for fault tolerant numerical computing engines, as used in many critical applications with long computation/mission times, is becoming pronounced. In this paper, we present an algorithm-based fault tolerance (ABFT) scheme for an iterative linear solver engine based on the Conjugated Gradient method (CG) by taking the advantage of numerical defect correction. This method is "pay as you go", meaning that there is practically only a runtime overhead if errors occur and a correction is performed. Our experimental comparison with software-based Triple Modular Redundancy (TMR) clearly shows the runtime benefit of the proposed approach, good fault tolerance and no occurrence of silent data corruption.

关键词： algorithm-based fault tolerance defect correction conjugated gradient triple modular redundancy checkpointing

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