FT-EALU：关键嵌入式和实时的系统的容错运算器
FT-EALU: Fault Tolerant Arithmetic and Logic Unit for Critical Embedded and Real time Systems
摘要：
本文提出了一种嵌入式处理器的容错方法FT-EALU，用于消除嵌入式处理器的算术和逻辑运算的瞬时和永久故障。在这种方法中，每个操作都被及时复制，并对派生的最终结果进行投票以生成最终输出。为了考虑永久性故障的影响，及时复制相同的操作是不够的，需要使操作数多样化。为了实现这一目标，在FT-EALU中，我们考虑了三个不同版本的输入数据，并将目标操作按时间顺序应用于它们。为了避免高的时间开销，我们使用简单的操作符，如移位和交换，使一个适当的转移在输入数据。我们提出的容错方法通过复制和不同的结果，一个新的加权投票器，是基于奖励/惩罚策略。对于每一个版本的执行，根据建议的加权方法，相应的权重，根据其纠正能力面临的几个错误的情况下定义。该权重定义了每个执行版本的结果的可靠性，并确定其对最终结果的影响。最终结果是根据每次执行的权重及其计算结果逐位生成的。这些权重通过设计时学习方案根据在各种数据位上应用几种类型的故障来静态地确定。根据执行版本缓解永久性故障的能力，为它们分配正或负分数。这些分数在几种情况下进行积分，并进行归一化，以获得每个执行位级别的适当权重。实验结果表明，FT-EALU对单比特和双比特输入数据的永久注入故障的纠正率分别为84.93%和69.71%。
英文摘要：
In this paper, a fault-tolerant approach to mitigate transient and permanent faults of arithmetic and logic operations of embedded processors called FT-EALU is proposed. In this method, each operation is replicated in time and the derived final results are voted to generate the final output. To consider the effect of permanent faults, replicating identical operations in time is not sufficient, and diversifying the operands is required. To this aim in FT-EALU, we consider three distinct versions of input data and apply the target operation to them serially in time. To avoid high time overhead, we employ simple operators such as shift and swap to make an appropriate diversion in input data. Our proposed fault tolerance approach passes the replicated and diverse results to a novel weighted voter that is designed based on the reward/punishment strategy. For each version of execution, based on the proposed weighting approach a corresponding weight according to its correction capability confronting several faulty scenarios is defined. This weight defines the reliability of the result of each version of execution and determines its effect on the final result. The final result is generated bit by bit based on the weight of each execution and its computed result. These weights are determined statically through a design-time learning scheme according to applying several types of faults on various data bits. Based on the capability of execution versions on mitigating the permanent faults, positive or negative scores are assigned to them. These scores are integrated for several cases and normalized to derive the appropriate weight of each execution at bit level. Several experiments are performed to show the efficiency of our proposed approach and based on them, FT-EALU is capable of correcting about 84.93% and 69.71% of permanent injected faults on single and double bits of input data.
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