Chiplets are a promising architecture for high-performance AI computing, but their package-level interconnects create a tightly coupled multiphysics problem involving signal delivery, power delivery, and heat dissipation. This challenge is compounded by the need to co-optimize the interposer and substrate, which have divergent design rules and performance sensitivities. To address these challenges, we propose MIP-SPT, a framework for multi-level interconnect planning. We introduce a hierarchical variable sched- uling strategy that decouples interposer and substrate variables, significantly reducing the search space. MIP-SPT then employs a multi-phase Bayesian optimization scheme to fully explore the streamlined design space. Crucially, our framework quantitatively models the effects of multiphysics coupling during planning to achieve rapid design closure. Experimental results show that our work reduces manufacturing cost by 22.4% compared to the baseline single-phase Bayesian optimization under equivalent design con- straints. In addition, it outperforms two existing works, lowering interconnect cost by 23.1% and 18.1%, respectively.

This article reviews published research in the field of computer-aided colorization technology. We argue that within this context, the colorization task can be considered to originate from computer graphics, advance by introducing computer vision, and progress towards the fusion of vision and graphics. Hence, we propose a specific taxonomy and organize the research work chronologically. We extend the existing reconstruction-based colorization evaluation techniques on the basis that aesthetic assessment should be introduced to ensure the computer-coloredimages closely satisfy human visual-related requirements. We then perform an aesthetic assessment using the proposed metric and existing evaluations, comparing the colorization performance of seven representative unconditional colorization models. Finally, we identify unresolved issues and propose fruitful areas for future research and development.

This paper presents a new algorithm, Weighted Squared Volume Minimization (WSVM), for generating high-quality tetrahedral meshes from closed triangle meshes. Drawing inspiration from the principle of minimal surfaces that minimize squared surface area, WSVM employs a new energy function integrating weighted squared volumes for tetrahedral elements. When minimized with constant weights, this energy promotes uniform volumes among the tetrahedra. Adjusting the weights to account for local geometry further achieves uniform dihedral angles within the mesh. The algorithm begins with an initial tetrahedral mesh generated via Delaunay tetrahedralization and proceeds by sequentially minimizing volume-oriented and then dihedral angle-oriented energies. At each stage, it alternates between optimizing vertex positions and refining mesh connectivity through the iterative process. The algorithm operates fully automatically and requires no parameter tuning. Evaluations on a variety of 3D models demonstrate that WSVM consistently produces tetrahedral meshes of higher quality, with fewer slivers and enhanced uniformity compared to existing methods.

Being essential in animation creation, colorizing anime line drawings is usually a tedious and time-consuming manual task. Reference-based line drawing colorization provides an intuitive way to automatically colorize target line drawings using reference images. The prevailing approaches are based on generative adversarial networks (GANs), yet these methods still cannot generate high-quality results comparable to manually-colored ones. In this article, a new AnimeDiffusion approach is proposed via hybrid diffusions for the automatic colorization of anime face line drawings. This is the first attempt to utilize the diffusion model for reference-based colorization, which demands a high level of control over the image synthesis process. To do so, a hybrid end-to-end training strategy is designed, including phase 1 for training diffusion model with classifier-free guidance and phase 2 for efficiently updating color tone with a target reference colored image. The model learns denoising and structure-capturing ability in phase 1, and in phase 2, the model learns more accurate color information. Utilizing our hybrid training strategy, the network convergence speed is accelerated, and the colorization performance is improved. Our AnimeDiffusion generates colorization results with semantic correspondence and color consistency. In addition, the model has a certain generalization performance for line drawings of different line styles. To train and evaluate colorization methods, an anime face line drawing colorization benchmark dataset, containing 31,696 training data and 579 testing data, is introduced and shared. Extensive experiments and user studies have demonstrated that our proposed AnimeDiffusion outperforms state-of-the-art GAN-based methods and another diffusion-based model, both quantitatively and qualitatively.