tailieunhanh - Báo cáo hóa học: "A framework for ABFT techniques in the design of fault-tolerant computing systems"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài :bA framework for ABFT techniques in the design of fault-tolerant computing systems | Hamidi et al. EURASIP Journal on Advances in Signal Processing 2011 2011 90 http content 2011 1 90 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access A framework for ABFT techniques in the design of fault-tolerant computing systems Hodjat Hamidi Abbas Vafaei and Seyed Amirhassan Monadjemi Abstract We present a framework for algorithm-based fault tolerance ABFT methods in the design of fault tolerant computing systems. The ABFT error detection technique relies on the comparison of parity values computed in two ways. The parallel processing of input parity values produce output parity values comparable with parity values regenerated from the original processed outputs. Number data processing errors are detected by comparing parity values associated with a convolution code. This article proposes a new computing paradigm to provide fault tolerance for numerical algorithms. The data processing system is protected through parity values defined by a high-rate real convolution code. Parity comparisons provide error detection while output data correction is affected by a decoding method that includes both round-off error and computer-induced errors. To use ABFT methods efficiently a systematic form is desirable. A class of burst-correcting convolution codes will be investigated. The purpose is to describe new protection techniques that are easily combined with data processing methods leading to more effective fault tolerance. Keywords algorithm-based fault tolerance ABFT burst-correcting convolution codes parity values syndrome 1. Introduction Algorithm-based fault tolerance ABFT was first introduced by Huang and Abraham 1 and was directed toward detection of high-level errors because of internal processing failures. ABFT techniques are most effective when employing a systematic form 2-6 . The motivational model basic ABFT as applied to data processing of blocks of real data is shown in Figures 1 and 2. The

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