论文标题
在多核心RISC-V平台上对中子引起的错误的实验评估
Experimental evaluation of neutron-induced errors on a multicore RISC-V platform
论文作者
论文摘要
RISC-V架构由于其灵活性和开源指令集体系结构（ISA）而在过去几年中变得非常重要，从而使开发人员能够在成本降低的几个域中有效地采用RISC-V处理器。对于诸如关键安全和关键任务的应用领域，执行必须可靠，因为故障会损害系统正确操作的能力。但是，该应用程序对RISC-V处理器的错误率没有得到显着评估，因为已经针对标准X86处理器进行了错误。在这项工作中，我们研究了暴露于中子束的商业RISC-V ASIC平台GAP8的错误率。我们表明，对于计算密集型应用，例如分类卷积神经网络（CNN），错误率可能比平均错误率高3.2倍。此外，我们发现，CNN上的大多数（96.12％）不会产生错误分类。最后，我们还评估了导致GAP8上中断应用中断的事件，并表明错误中断的主要来源是应用程序挂起（I.G.，由于无限循环或赛车条件）。
RISC-V architectures have gained importance in the last years due to their flexibility and open-source Instruction Set Architecture (ISA), allowing developers to efficiently adopt RISC-V processors in several domains with a reduced cost. For application domains, such as safety-critical and mission-critical, the execution must be reliable as a fault can compromise the system's ability to operate correctly. However, the application's error rate on RISC-V processors is not significantly evaluated, as it has been done for standard x86 processors. In this work, we investigate the error rate of a commercial RISC-V ASIC platform, the GAP8, exposed to a neutron beam. We show that for computing-intensive applications, such as classification Convolutional Neural Networks (CNN), the error rate can be 3.2x higher than the average error rate. Additionally, we find that the majority (96.12%) of the errors on the CNN do not generate misclassifications. Finally, we also evaluate the events that cause application interruption on GAP8 and show that the major source of incorrect interruptions is application hangs (i.g., due to an infinite loop or a racing condition).
