As integrated circuit (IC) designs continue to scale in complexity, global routing faces increasing challenges in managing timing and congestion simultaneously. This paper proposes a GPU-accelerated global routing framework that effectively balances timing optimization and congestion mitigation. The proposed framework begins with a preprocessing stage that partitions ultralarge nets, followed by timing path construction, and decomposes nets based on estimated pin slack to enhance scalability and timing sensitivity. For critical nets, we propose a timing and congestion driven GPU-accelerated hybrid 3D pattern routing method. Specifically, an Elmore-based timing weight calculation method is proposed to efficiently capture the timing criticality of routing paths, and the resulting weights are ordered for more targeted and effective timing optimization. Then, a well-designed cost scheme is proposed to better balance timing and congestion. Finally, we develop a GPU-accelerated hybrid 3D pattern routing strategy that combines L -shape and sparse Z -shape patterns to improve routing efficiency. After routing critical nets, the remaining non-critical nets are routed using a congestion-driven GPU-accelerated routing engine that supports flexible detours to alleviate congestion and utilize residual routing resources. Compared with the champion of the ISPD 2025 contest, experimental results on the ISPD 2025 contest benchmarks show that our algorithm achieves 19.4% better weighted scores and 1 6. 2 % faster runtime.

In this study, we propose a neural network-based face anti-spoofing algorithm using dual pixel (DP) sensor images. The proposed algorithm has two stages: depth reconstruction and depth classification. The first network takes a DP image pair as input and generates a depth map with a baseline of approximately 1 mm. Then, the classification network is trained to distinguish real individuals and planar attack shapes to produce a binary output. A DP image is utilized to estimate the depth map; thus, the proposed face anti-spoofing method is simple and robust. Experimental results demonstrate that the generated depth map helps distinguish real human faces from nonface attack, including images recaptured from photos or screens. The proposed algorithm achieves better anti-spoofing performance compared with other stereo and phase-based depth estimation schemes.

Cross-domain data has become very popular recently since various viewpoints and different sensors tend to facilitate better data representation. In this article, we propose a novel cross-domain object representation algorithm (RLRCA) which not only explores the complexity of multiple relationships of variables by canonical correlation analysis (CCA) but also uses a low rank model to decrease the effect of noisy data. To the best of our knowledge, this is the first try to smoothly integrate CCA and a low-rank model to uncover correlated components across different domains and to suppress the effect of noisy or corrupted data. In order to improve the flexibility of the algorithm to address various cross-domain object representation problems, two instantiation methods of RLRCA are proposed from feature and sample space, respectively. In this way, a better cross-domain object representation can be achieved through effectively learning the intrinsic CCA features and taking full advantage of cross-domain object alignment information while pursuing low rank representations. Extensive experimental results on CMU PIE, Office-Caltech, Pascal VOC 2007, and NUS-WIDE-Object datasets, demonstrate that our designed models have superior performance over several state-of-the-art cross-domain low rank methods in image clustering and classification tasks with various corruption levels.