Posit数据表示的内在鲁棒性研究
An Investigation on Inherent Robustness of Posit Data Representation
摘要：
随着计算机电子设备的尺寸和工作电压缩小以满足消费者对更高性能和更低功耗的需求，电路对软错误的敏感性急剧增加。最近，在文献中提出了一种新的数据类型，称为数据类型数据类型。Posit算法比IEEE 754-2008技术标准兼容算法具有更高的数值精度、速度和更简单的硬件设计等绝对优势。在本文中，我们提出了一个比较鲁棒性的研究32位的IEEE 754-2008兼容的表示和32位之间。首先，我们提出了一个理论分析的IEEE 754兼容的号码和单比特翻转和双比特翻转的随机数。然后，我们进行了详尽的故障注入实验，表现出相当大的内在弹性在ESTA格式相比，经典的IEEE 754兼容的表示。为了展示容错应用程序的相关用例，我们对一组机器学习应用程序进行了实验。在超过95%的详尽故障注入探索中，与IEEE 754兼容的浮点表示相比，EVM表示受故障的影响较小。此外，在100%的测试机器学习应用程序中，位置实现系统的准确性高于经典的基于浮点的系统。
英文摘要：
As the dimensions and operating voltages of computer electronics shrink to cope with consumers' demand for higher performance and lower power consumption, circuit sensitivity to soft errors increases dramatically. Recently, a new data-type is proposed in the literature called posit data type. Posit arithmetic has absolute advantages such as higher numerical accuracy, speed, and simpler hardware design than IEEE 754-2008 technical standard-compliant arithmetic. In this paper, we propose a comparative robustness study between 32-bit posit and 32-bit IEEE 754-2008 compliant representations. At first, we propose a theoretical analysis for IEEE 754 compliant numbers and posit numbers for single bit flip and double bit flips. Then, we conduct exhaustive fault injection experiments that show a considerable inherent resilience in posit format compared to classical IEEE 754 compliant representation. To show a relevant use-case of fault-tolerant applications, we perform experiments on a set of machine-learning applications. In more than 95% of the exhaustive fault injection exploration, posit representation is less impacted by faults than the IEEE 754 compliant floating-point representation. Moreover, in 100% of the tested machine-learning applications, the accuracy of posit-implemented systems is higher than the classical floating-point-based ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
