The increasing complexity of high-frequency circuits calls for efficient and accurate passive macromodeling techniques. Existing passivity enforcement methods, including those in commercial tools, often encounter convergence issues or compromise accuracy. The Domain-Alternated Optimization (DAO) framework seeks to restore accuracy through an additional optimization step but is hampered by high memory consumption and slow convergence, particularly for large-scale problems. This paper presents G-SpNN, a novel GPU-accelerated framework that recasts the passivity-enforced macromodeling problem as a neural network training task. This approach significantly enhances both the speed and scalability of passivity enforcement. Experimental results show that G-SpNN achieves an average speedup of $7.63 \times$ in convergence compared to DAO, while reducing memory usage by two orders of magnitude. This enables G-SpNN to handle complex, high-port-count circuits with greater accuracy and efficiency, paving the way for robust high-frequency circuit simulations.

Pseudo transient analysis (PTA) has been a promising solution for direct current (DC) analysis of transistor-level circuit simulation. Despite its popularity, PTA requires meticulous hyperparameter tuning for optimal performance. In this paper, we propose pseudo adjoint optimization, Soda-PTA, which models the PTA solution curve (which is used to measure convergence) using a neural ordinary differential equation (Neural ODE) and deriving explicit gradients of the Newton-Raphson (NR) iteration w.r.t. the PTA hyperparameters through the classic adjoint method, enabling effective optimization of the PTA hyperparameters. To generalize Soda-PTA for unseen circuits, we further introduce a graph convolution network to transfer optimal PTA hyperparameters from the other circuits to the target one. Soda-PTA is implemented in an out-of-the-box SPICE simulator. Through extensive experiments, Soda-PTA demonstrates superior acceleration performance: an average speedup of 1.53x over the state-of-the-art BoA-PTA while ensuring superior convergence and up to 22.12x speedup compared to the native PTA solver.

Software vulnerability detection in source code is a fundamental problem in cyber-security. Aiming at discovering the vulnerability automatically, this paper proposes an open source software vulnerability detection method based on attention-based bidirectional recurrent neural networks. Based on the high-level and generalizable function representations that obtained from the abstract syntax tree(AST), an attention-based bidirectional recurrent neural networks is devised to capture the sequential and important code elements in vulnerability detection from the large number of features that the deep learning model has learned. Experimental results confirm that the huge potential of the proposed new vulnerability detection method which is not only more effective than Convolutional Neural Networks(CNN) but also better than traditional Bidirectional Recurrent Neural Networks(BRNN) in reducing the false negative rate at the price of increasing the false positive rate.