With the continued scaling of integrated circuits (ICs), IR drop analysis for on-chip power grids (PGs) is crucial but increasingly computationally demanding. Traditional numerical methods deliver high accuracy but are prohibitively time-intensive, while various machine learning (ML) methods have been introduced to alleviate these computational burdens. However, most CNN-based methods ignore the fine structure and topological information of PGs, and face interpretability or scalability issues. In this work, we propose a novel graphbased framework, IRGNN, leveraging the PG topology with the integration of numerical solutions and point clouds. Our framework applies a numerical solver, AMG-PCG, to generate rough numerical solutions as a reliable interpretability foundation for ML. Then, to capture PG topology, we regard nodes of PG as point clouds and extract point cloud features, and we introduce a novel graph structure, IRGraph. Furthermore, a novel graph-based model IRGNN is designed, incorporating a designed neighbor distance attention (NDA) layer for distanceaware PG features aggregation and graph transformer (GT) layer to capture global information. It should be noted that our framework can analyze the IR drop of each node in PG, which CNN-based methods cannot do. Experimental evaluations demonstrate that our framework achieves significantly higher accuracy than previous CNN-based approaches and numerical solvers while substantially reducing computation time.

Code readability is a critical aspect of program comprehension and an important metric for overall software quality.Recently, many deep learning-based code readability classification models have been proposed.Although they reached state-ofthe-art classification results, we consider that their performance is limited due to the shortage of labeled data.To address this problem, we propose WCL-CRC, a two-stage weakly-supervised contrastive learning framework for few-shot code readability classification, which pre-trains existing models using contrastive learning techniques with a large amount of weakly-labeled data obtained from open-source repositories and then fine-tunes them with a few labeled data.We conduct experiments on Java and Python datasets.The results show that applying WCL-CRC to existing code readability classification models can improve their Accuracy by 0.21% to 2.43%, F-Measure by 0.52% to 2.66%, AUC by 0.14% to 1.58%, and MCC by 1.06% to 8.73%, indicating that our approach effectively learns readability-related information from a large amount of weakly-labeled data and significantly improves code readability classification performance in situations where labeled data is limited.