论文标题
系统的无监督可回收的现场可编程栅极阵列检测
Systematic Unsupervised Recycled Field-Programmable Gate Array Detection
论文作者
论文摘要
随着半导体供应链的扩展，可回收的现场可编程栅极阵列（FPGA）的使用已成为一个严重的问题。已经提出了几种通过分析环振荡器（RO）频率来检测回收FPGA的方法。但是，大多数人以基于机器学习的分类中已知的新鲜FPGA（KFF）为训练数据。在这项研究中，我们提出了一种新型的回收FPGA检测方法，基于无监督的异常检测方案，当时很少或没有KFF。由于系统的过程变化，由于相邻的逻辑块中的RO频率相似，因此我们的方法比较了RO频率，并且不需要KFF。该方法使用直接密度比估计有效地通过离群值检测有效地识别了回收的FPGA。使用Xilinx Artix-7 FPGA的实验表明，该提出的方法成功区分了35个新鲜FPGA的回收FPGA。相反，常规的回收FPGA检测方法导致某些错误分类。
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.
