Circuit recognition with deep learning
Circuit recognition with deep learning
复制标题
通过深度学习进行电路识别
DOI：
10.1109/hst.2017.7951826
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
R. Brayton
中科院分区：
文献类型：
--
作者：
Yu;R. Brayton
识别电路的属性(特征)并应用适当的算法有助于解决各种计算机辅助设计问题。对于硬件安全检查，需要反向工程，即从位级设计中提取高级组件的过程。给出一个可疑的电路块，通常的方法是找到一组候选函数，然后应用形式化方法来识别它。识别高层函数的有用特征和收集未知块的建议候选是重要的步骤。卷积神经网络(CNN)在机器学习中得到了广泛的应用，因为通常不需要预定义的特征。具有多个处理层的深度网络已被证明能够在训练过程中学习对象的隐藏结构。本文讨论了用于CNN处理的表示逻辑电路的要求。提出了一种新的电路表示(数据格式)，用于所提出的基于电路的动态合并卷积运算。在此数据格式的基础上，建立了基于神经网络识别电路功能的深度学习框架。与已有的基于支持向量机的方法相比，实验证明了该方法在电路分类和功能检测与定位方面的有效性。利用适当的训练数据，例如一组带有隐藏特洛伊木马的电路，所提出的框架可用于训练模型以帮助检测和定位硬件设计中的恶意软件。
Identifying properties (features) of circuits and applying proper algorithms are helpful for solving various computer-aided design problems. For hardware security inspection, there is a demand for reverse engineering, the process of extracting high-level components from bit-level designs. Given a suspected circuit block, a common approach is to find a set of candidate functions and then to apply formal methods to identify it. Identifying useful features of high-level functions and collecting suggested candidates of an unknown block are important steps. Convolutional neural networks (CNNs) have been used extensively in machine learning because often pre-defined features are not required. Deep networks with multiple processing layers have been shown to be capable of learning concealed structures of objects during a training process. This paper discusses requirements for representing logic circuits for CNN processing. A new circuit representation (data format) is developed for the proposed circuit-based convolution operation with dynamic pooling. Based on this data format, a deep learning framework using CNNs to recognize circuit functionalities was built. Compared to reference methods based on support vector machines (SVM), experiments demonstrate the effectiveness of the proposed CNN method for both circuit classification as well as function detection and location. With proper training data, e.g. a set of circuits with hidden Trojans, the proposed framework can be used to train a model to help detect and locate malware in hardware designs.