系统无监督可编程现场门阵列检测。
Systematic Unsupervised Recycled Field-Programmable Gate Array Detection
摘要：
随着半导体供应链的扩展，回收的现场可编程门阵列（FPGA）的使用已成为一个严重的问题。已经提出了通过分析环形振荡器（RO）频率来检测回收的FPGA的几种方法;然而，大多数假设已知的新鲜FPGA（KFF）作为基于机器学习的分类中的训练数据。在这项研究中，我们提出了一种新的回收FPGA检测方法的基础上，无监督的异常检测方案时，有很少或没有可用的KFF。由于FPGA上相邻逻辑块中的RO频率由于系统工艺变化而相似，因此我们的方法比较RO频率并且不需要KFF。所提出的方法有效地识别回收FPGA通过离群值检测使用直接密度比估计。使用Xilinx Artix-7 FPGA的实验表明，所提出的方法成功地区分回收的FPGA和35个新鲜的FPGA。相比之下，传统的回收FPGA检测方法会导致一定的误分类。
英文摘要：
With the expansion of the semiconductor supply chain, the use of recycled field-programmable gate arrays (FPGAs) has become a serious concern. Several methods for detecting recycled FPGAs by analyzing the ring oscillator (RO) frequencies have been proposed; however, most assume the known fresh FPGAs (KFFs) as the training data in machine-learning-based classification. In this study, we propose a novel recycled FPGA detection method based on an unsupervised anomaly detection scheme when there are few or no KFFs available. As the RO frequencies in the neighboring logic blocks on an FPGA are similar because of systematic process variation, our method compares the RO frequencies and does not require KFFs. The proposed method efficiently identifies recycled FPGAs through outlier detection using direct density ratio estimation. Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method successfully distinguishes recycled FPGAs from 35 fresh FPGAs. In contrast, a conventional recycled FPGA detection method results in certain misclassification.
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。
