As dynamic power has become the bottleneck to achieving low power, clock power reduction is crucial in modern IC design. Register clustering can effectively save clock power because of significantly reducing the number of clock sinks and register pin capacitance, clock routed wirelength, and the number of clock buffers. In this article, we propose effective mean shift to naturally form clusters according to register distribution without placement disruption. Effective mean shift fulfills the requirements to be a good register clustering algorithm because it needs no prespecified number of clusters, is insensitive to initializations, is robust to outliers, is tolerant of various register distributions, is efficient and scalable, and balances clock power reduction against timing degradation. Furthermore, we devise a rebanking mechanism to legalize the clustering result according to the adopted multibit register library. Experimental results show that our approach achieves superior power and timing balancing.

In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the intermediate region between them. Although such problem is closely related to the topics of image inpainting, image outpainting, and image blending, none of the approaches from these topics is able to easily address it. We introduce an effective deep-learning model to realize wide-range image blending, where a novel Bidirectional Content Transfer module is proposed to perform the conditional prediction for the feature representation of the intermediate region via recurrent neural networks. In addition to ensuring the spatial and semantic consistency during the blending, we also adopt the contextual attention mechanism as well as the adversarial learning scheme in our proposed method for improving the visual quality of the resultant panorama. We experimentally demonstrate that our proposed method is not only able to produce visually appealing results for wide-range image blending, but also able to provide superior performance with respect to several baselines built upon the state-of-the-art image inpainting and outpainting approaches.

Removing undesired reflection from an image captured through a glass window is a notable task in computer vision. In this paper, we propose a novel model with auxiliary techniques to tackle the problem of single image reflection removal. Our model takes a reflection contaminated image as input, and decomposes it into the reflection layer and the transmission layer. In order to ensure quality of the transmission layer, we introduce three auxiliary techniques into our architecture, including the edge guidance, a reflection classifier, and the recurrent decomposition. The contributions and the efficacy of these techniques are investigated and verified in the ablation study. Furthermore, in comparison to the state-of-the-art baselines of reflection removal, both quantitative and qualitative results demonstrate that our proposed method is able to deal with different kinds of images, achieving the best results in average.

As the wide adoption of FinFET technology in mass production, dynamic power becomes the bottleneck to achieving low power. Therefore, clock power reduction is crucial in modern IC design. Register clustering can effectively save clock power because of significantly reducing the number of clock sinks and register pin capacitance, clock routed wirelength, and the number of clock buffers. In this paper, we propose effective mean shift to naturally form clusters according to register distribution without placement disruption. Effective mean shift fulfills the requirements to be a good register clustering algorithm because it needs no prespecified number of clusters, is insensitive to initializations, is robust to outliers, is tolerant of various register distributions, is efficient and scalable, and balances clock power reduction against timing degradation. Experimental results show that our approach outperforms state-of-the-art work on power and timing balancing, as well as efficiency and scalability.