In the post-layout simulation for large-scale integrated circuits, Transient Analysis (TA), determining the time-domain response over a specified time interval, is essential and time-consuming. Especially, a mass of backward steppings and low simulation efficiency occur without proper settings of Newton-Raphson (NR) initial solution and accurate Local Truncation Error (LTE) estimation. In this work, a novel multi-step irregular sequence prediction model (MISP-Net) is proposed to predict multiple NR initial solutions and precise LTE estimations by just one inference step. This model is constructed by an Irregular Multiple Timesteps Prediction Module (IMTP) and a Irregular Multi-step Solution Prediction Module (IMSP). In IMSP, to improve the irregular prediction performance, a Dual-branch Irregular Feature Pyramid (DIFP) equipped with lightweight Multi-Channel Irregular Time Attention (MITA) are designed. We assess the proposed MISP-Net in the real large-scale industrial circuits on a commercial SPICE simulator. Compared with the commercial SPICE and the SOTA ISPT-Net model, significant backward stepping reductions are achieved: up to 78.57% for NR nonconvergence case and 76.62% for LTE overlimit case, respectively. And the prediction time for NR initial solution in our model is remarkably reduced by up to 5.58× compared to the SOTA ISPT-Net model.

DC analysis holds critical importance in nonlinear circuit simulation, providing the essential precondition for transient and AC analyses. While Pseudo-Transient Analysis (PTA) and its variants excel in DC analysis, selecting the optimal PTA method for specific circuits remains challenging. To address this, we propose GE-LLM, a novel framework for optimal PTA method selection, which integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs). The framework first converts circuit netlists into graph representations and employs a GNN-based graph encoder to capture essential circuit topologies. Subsequently, a novel text-graph alignment strategy bridges circuit topologies and textual descriptions, enabling the LLM to effectively comprehend multimodal information. Finally, we introduce a multi-perspective few-shot prompt that mitigates data scarcity by enabling effective in-context learning from limited circuit examples. Experimental results demonstrate that GE-LLM achieves a high selection accuracy of 0.9714 and improves the efficiency of DC analysis, yielding an average speedup of 2.89× in PTA steps (up to 12.14×) and 3.45× in Newton-Raphson iterations (up to 30.39×) compared to a commercial SPICE-like simulator.

IR drop analysis is crucial for ensuring the reliability and performance of integrated circuits (ICs) but poses computational challenges as the IC designs grow larger, especially for ultra deep-submicron VLSI designs. Deep learnings (DL) as the efficiency-promising solutions, mainly employ various CNN-based networks to achieve image-to-image IR drop predictions. However, they neglect and lose the power delivery network (PDN) global spatial features and cell instance topological information. This paper proposes a novel image-graph heterogeneous fusion framework (IGHF), which integrates the effectiveness and complementarity of dual branches (CNN and GNN) for higher prediction performance. In the CNN-based Power ScaleFusion Unet branch, the proposed long-range and local-detail encoder (LLE) integrates seamlessly with the hierarchical and adjacent compensation group (HACG) module. This design facilitates effective multi-scale global-to-local spatial power feature extraction within the PDN and enables adaptive high-to-low-level feature fusion and compensation in the decoder. Moreover, a cell voltage aware (CVA) module in the GNN branch is designed to adaptively aggregate PDN topological features of heterogeneous neighbors of different orders. Comparative experiments demonstrate that the proposed IGHF achieves significant accuracy improvements, outperforming the state-of-the-art MAUNet and widely-used IREDGe methods by considerable margins of 24.6% and 55.0% reduction in prediction error, while the prediction maps possess higher structural fidelity. Transfer experiments indicate that IGHF with transfer learning can improve the accuracy in real circuits with the few-shot real circuit test cases.

In the post-Moore era, 2.5D chiplet-based ICs present significant challenges in thermal management due to increased power density and thermal hotspots. Neural network-based thermal prediction models can perform real-time predictions for many unseen new designs. However, existing CNN-based and GCN-based methods cannot effectively capture the global thermal features, especially for high-frequency components, hindering pre-diction accuracy enhancement. In this paper, we propose a novel frequency-spatial dual domain aware prediction network (FSA-Heat) for fast and high-accuracy thermal prediction in 2.5D ICs. It integrates high-to-low frequency and spatial domain encoder (FSTE) module with frequency domain cross-scale interaction module (FCIFormer) to achieve high-to-low frequency and global-to-local thermal dissipation feature extraction. Additionally, a frequency-spatial hybrid loss (FSL) is designed to effectively attenuate high-frequency thermal gradient noise and spatial mis-alignments. The experimental results show that the performance enhancements offered by our proposed method are substantial, outperforming the newly-proposed 2.5D method, GCN+PNA, by considerable margins (over 99% RMSE reduction, 4.23X inference time speedup). Moreover, extensive experiments demonstrate that FSA-Heat also exhibits robust generalization capabilities.

Thermal management in large-scale unstructured 2.5D ICs faces the challenges due to the integration of complex geometries and heterogeneous materials. Existing deep learning (DL) methods urgently require a memory-efficient and high-fidelity unstructured representation method for multiscale complex ICs to simultaneously model macroscopic components and microscopic structure. Moreover, it further needs to achieve multiscale geometric thermal feature capture and thermal distribution difference adaptation among heterogeneous materials. Combining a multiscale unstructured point-cloud representation, this paper introduces Therm-PCT, a geometry-material aware point-cloud transformer framework to achieve high-accuracy thermal and its gradient prediction. Therm-PCT incorporates three key modules: adaptive multipath-coupled diffusion (AMD), a wavelet-based fine-grained recovery (WFR), and a thermal-aware Mixture-of-Material-Experts (TA-MoME) adapter. AMD adaptively learns heat diffusion path interaction with serialization-gate-based attention. Furthermore, the WFR module recovers fine-grained thermal gradients through high-frequency wavelet domain enhancement, and the TA-MoME adapter adapts to heterogeneous material by dynamically routing material-specific experts. Experiments demonstrate that the Thermal-PCT’s accuracy performance metric improvements are substantial, outperforming the newly proposed method FSA-Heat, by considerable margins of 78.03%, 84.00%, 67.61%, and 78.25% in 80 K-scale point clouds. It also achieves a 147× speed-up compared to the commercial software COMSOL. Additionally, Therm-PCT shows the potential of zero-shot generalization up to 0.4 M-scale points (5.7× than training scale) and robust performance on unseen geometric shapes.

Direct current (DC) analysis lies at the heart of integrated circuit design in seeking DC operating points. Although pseudo-transient analysis (PTA) methods have been widely used in DC analysis in both industry and academia, their initial parameters and stepping strategy require expert knowledge and labor tuning to deliver efficient performance, which hinders their further applications. In this paper, we leverage the latest advancements in machine learning to deploy PTA with more efficient setups for different problems. More specifically, active learning, which automatically draws knowledge from other circuits, is used to provide suitable initial parameters for PTA solver, and then calibrate on-the-fly to further accelerate the simulation process using TD3-based reinforcement learning (RL). To expedite model convergence, we introduce dual agents and a public sampling buffer in our RL method to enhance sample utilization. To further improve the learning efficiency of the RL agent, we incorporate imitation learning to improve reward function and introduce supervised learning to provide a better dual-agent rotation strategy. We make the proposed algorithm a general out-of-the-box SPICE-like solver and assess it on a variety of circuits, demonstrating up to 3.10× reduction in NR iterations for the initial stage and 285.71× for the RL stage.

In the post-layout simulation for large-scale integrated circuits, transient analysis (TA), determining the time-domain response over a specified time interval, is essential and important. However, it tends to be computationally intensive and quite time-consuming without proper settings of NR initial solution and accurate LTE estimation for determining the next transient timestep, which will lead to a mass of backward-steppings. In this paper, an irregular sequential prediction transformer named ISPT-Net is proposed to predict accurately transient solution as NR initial solution and further obtain precise LTE estimation for setting next timestep. The ISPT-Net is strengthened with timestep positional encoding module (TPE), frequency- and timestep-sensitive muti-head self-attention module (FT-MSA) to enhance irregular sequence feature extraction and prediction accuracy. We assess ISPT-Net in the real large-scale industrial circuits on a commercial SPICE simulator, and achieve a remarkable backward stepping reduction: up to 14.43X for NR nonconvergence case and 4.46X for LTE overlimit case while guaranteeing higher solution accuracy.

Sparse LU factorization is a vital technique in solving circuit linear equations, However, irregular data access patterns contribute to unsatisfactory computational efficiency and excessive memory usage. Conventional LU factorization methods generally involve two approaches: either they utilize space-intensive dense matrices for direct index-to-data mapping, or they inefficiently scour through indices to locate the positions of updated data elements. To resolve these challenges, we propose the Indexing-Efficient Sparse LU factorization (ISLU) in this work. A novel indexing-efficient member union is put forwarded to achieve efficient retrieval of indices within compressed formats, thereby significantly enhancing the LU decomposition efficiency. Furthermore, to expedite the establishment of indexing-efficient member union, we design, for the first time, parallel creating member union strategy for GPU platforms, which remarkably reduces the time overhead associated with constructing the proposed structures. Extensive experimental comparisons on 49 benchmark matrices and real SPICE transient simulations demonstrate that the performance enhancements by our proposed ISLU method are substantial, outperforming various excellent GPU and CPU solvers including commercial solvers.

Chinese text matching is an important task in natural language processing research, but the current techniques have problems in text feature extraction, such as insufficient word information extraction and lack of deep information in graph convolution networks. In this paper, we propose a model MFG-R for Chinese text matching with multi-information fusion graph embedding and residual connection. The model fuses the word embedding representation of the text obtained by graph convolution network with character-level information and word weight information to extract text features. At the same time, in order to perform deep interaction matching, we construct a word-level similarity interaction matrix between text pairs, and build a text interaction and feature extraction model based on residual network on this basis. Experiments show that MFG-R has excellent performance on two common Chinese datasets, Ant Financial Question Matching Corpus(AFQMC) and Large-scale Chinese Question Matching Corpus(LCQMC).

The dc analysis is essential and still quite challenging in large-scale nonlinear circuit simulation. Pseudo transient analysis (PTA) is a widely used and has great potential solver in the industry. However, the PTA convergence and simulation efficiency is still seriously affected by its stepping policy. This article proposes an online stochastic stepping policy (OSSP) for PTA based on deep reinforcement learning (DRL). To achieve better policy evaluation and stronger stepping exploration ability, the dual soft Actor–Critic agents work with the proposed valuation splitting and online momental scaling, enabling our OSSP to intelligently encode PTA iteration status and online further adjust forward and backward time-step size for unseen test circuits without human intervention and domain knowledge, trained solely by reinforcement learning from self-search. Our public sample buffer and priority sampling are also introduced to overcome the sparsity and imbalance of sample data. Numerical examples demonstrate that the proposed OSSP achieves a significant efficiency speedup (up to$47.0\times $less Newton–Raphson iterations) and convergence enhancement on unseen test circuits compared with the previous iter-based and switched evolution/relaxation-based stepping methods, in just one stepping iteration.

DC analysis is the foundation for nonlinear electronic circuit simulation. Pseudo transient analysis (PTA) methods have gained great success among various continuation algorithms. However, PTA tends to be computationally intensive without careful tuning of parameters and proper stepping strategies. In this paper, we harness the latest advancing in machine learning to resolve these challenges simultaneously. Particularly, an active learning is leveraged to provide a fine initial solver environment, in which a TD3-based Reinforcement Learning (RL) is implemented to accelerate the simulation on the fly. The RL agent is strengthen with dual agents, priority sampling, and cooperative learning to enhance its robustness and convergence. The proposed algorithms are implemented in an out-of-the-box SPICElike simulator, which demonstrated a significant speedup: up to 3.1X for the initial stage and 234X for the RL stage.

As information sharing becomes more and more convenient, a lot of phenomena of plagiarism shows up. The study of cross-language plagiarism is an important problem that the whole academic circle tries to solve it collectively. In this paper, a multiple-features based cross-language plagiarism detection model is proposed, which includes cross-language plagiarism candidate retrieval based on multiple features and cross-language plagiarism detection based on dynamic text alignment. For cross-language plagiarism candidate retrieval, it is mainly based on the translation features. What's more, for cross-language plagiarism detection, a text-alignment based similarity analysis was used to filter the final results between the identified paragraphs. In this step, our approach doesn't use a machine translation system to convert longer text, but uses a dictionary to obtain the translation of a single word. Moreover, experimental results show that our method outperforms the previous methods and achieved the best results in four datasets.

Motivation: Advancements in next-generation sequencing technology have produced large amounts of reads at low cost in a short time. In metagenomics, 16S and 18S rRNA gene have been widely used as marker genes to profile diversity of microorganisms in environmental samples. Through clustering of sequencing reads we can determine both number of OTUs and their relative abundance. In many applications, clustering of very large sequencing data with high efficiency and accuracy is essential for downstream analysis. Results: Here, we report a scalable D irichlet Process Means (DP-means) a lgorithm for c lustering e xtremely large sequencing data, termed . With an efficient random projection partition strategy for parallel clustering, DACE can cluster billions of sequences within a couple of hours. Experimental results show that DACE runs between 6 and 80 times faster than state-of-the-art programs, while maintaining overall better clustering accuracy. Using 80 cores, DACE clustered the Lake Taihu 16S rRNA gene sequencing data (∼316M reads, 30 GB) in 25 min, and the Ocean TARA Eukaryotic 18S rRNA gene sequencing data (∼500M reads, 88 GB) into ∼100 000 clusters within an hour. When applied to the IGC gene catalogs in human gut microbiome (∼10M genes), DACE produced 9.8M clusters with 52K redundant genes in 1.5 hours of running time. Availability and Implementation: DACE is available at https://github.com/tinglab/DACE . Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.