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Classification-Based Improvement of Application Robustness and Quality of Service in Probabilistic Computer Systems

机译:基于分类的概率计算机系统中应用鲁棒性和服务质量的改进

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Future semiconductors no longer guarantee permanent deterministic operation. They are expected to show probabilistic behavior due to lowered voltages and shrinking structures.Compared to radiation-induced errors, probabilistic systems face increased error frequencies leading to unexpected bit-flips. Approaches like probabilistic CMOS provide methods to control error distributions which reduce the error probability in more significant bits. However, instructions handling control flow or pointers still expect deterministic operation, thus requiring a classification to identify these instructions.We apply our transient error classification to probabilistic circuits using differing voltage distributions. Static analysis ensures that probabilistic effects only affect unreliable operations which accept a certain level of impreciseness, and that errors in probabilistic components will never propagate to critical operations.To evaluate, we analyze robustness and quality-of-service of an H.264 video decoder. Using classification results, we map unreliable arithmetic operations onto probabilistic components of a simulated ARM-based architecture, while the remaining operations use deterministic components.
机译:未来的半导体不再保证永久的确定性操作。由于降低的电压和缩小的结构,它们预计将显示出概率行为。与辐射引起的错误相比,概率系统面临的错误频率增加,导致意外的比特翻转。诸如概率CMOS之类的方法提供了控制错误分布的方法,这些方法可以降低更高有效位中的错误概率。但是,处理控制流或指针的指令仍希望确定性操作,因此需要分类以识别这些指令。我们将瞬态错误分类应用于使用不同电压分布的概率电路。静态分析可确保概率效应只影响不可靠的操作,这些操作会接受一定程度的不精确度,并且概率组件中的错误将永远不会传播到关键操作。要进行评估,我们将分析H.264视频解码器的鲁棒性和服务质量。使用分类结果,我们将不可靠的算术运算映射到基于ARM的模拟体系结构的概率组件上,而其余运算则使用确定性组件。

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