Hardware trojan detection methods, based on machine learning (ML) techniques,
mainly identify suspected circuits but lack the ability to explain how the
decision was arrived at. An explainable methodology and architecture is
introduced based on the existing hardware trojan detection features. Results
are provided for explaining digital hardware trojans within a netlist using
trust-hub trojan benchmarks.

基于机器学习的硬件木马检测方法主要识别疑似电路，但缺乏解释决策的能力。基于现有硬件木马检测特征引入了一种可解释的方法和体系结构，通过使用信任中心木马基准对网络列表中的数字硬件木马进行解释和结果的提供。

具备硬件木马分类和解释能力的人工智能架构

An AI Architecture with the Capability to Classify and Explain Hardware  Trojans

The run-time for optimization tools used in chip design has grown with the
complexity of designs to the point where it can take several days to go through
one design cycle which has become a bottleneck. Designers want fast tools that
can quickly give feedback on a design. Using the input and output data of the
tools from past designs, one can attempt to build a machine learning model that
predicts the outcome of a design in significantly shorter time than running the
tool. The accuracy of such models is affected by the representation of the
design data, which is usually a netlist that describes the elements of the
digital circuit and how they are connected. Graph representations for the
netlist together with graph neural networks have been investigated for such
models. However, the characteristics of netlists pose several challenges for
existing graph learning frameworks, due to the large number of nodes and the
importance of long-range interactions between nodes. To address these
challenges, we represent the netlist as a directed hypergraph and propose a
Directional Equivariant Hypergraph Neural Network (DE-HNN) for the effective
learning of (directed) hypergraphs. Theoretically, we show that our DE-HNN can
universally approximate any node or hyperedge based function that satisfies
certain permutation equivariant and invariant properties natural for directed
hypergraphs. We compare the proposed DE-HNN with several State-of-the-art
(SOTA) machine learning models for (hyper)graphs and netlists, and show that
the DE-HNN significantly outperforms them in predicting the outcome of
optimized place-and-route tools directly from the input netlists. Our source
code and the netlists data used are publicly available at
this https URL

设计工具的运行时间随着芯片设计的复杂性增加而增长，这已成为一个瓶颈。我们提出了一种将网络列表表示为有向超图，并提出了一种用于有效学习（有向）超图的模型，与现有的图学习框架相比，DE-HNN 显著提高了设计优化工具的精确度。