科研成果 by Type: Conference Paper

In recent years, many researches focus on sound source localization based on neural networks, which is an appealing but difficult problem. In this paper, a novel time-domain end-to-end method for sound source localization is proposed, where the model is trained by two strategies with both cross entropy loss and mean square error loss. Based on the idea of multi-task learning, CNN is used as the shared hidden layers to extract features and DNN is used as the output layers for each task. Compared with SRP-PHAT, MUSIC and a DNN-based method, the proposed method has better performance.

In back-end analog/mixed-signal (AMS) design flow, well generation persists as a fundamental challenge for layout compactness, routing complexity, circuit performance and robustness. The immaturity of AMS layout automation tools comes to a large extent from the difficulty in comprehending and incorporating designer expertise. To mimic the behavior of experienced designers in well generation, we propose a generative adversarial network (GAN) guided well generation framework with a post-refinement stage leveraging the previous high-quality manually-crafted layouts. Guiding regions for wells are first created by a trained GAN model, after which the well generation results are legalized through post-refinement to satisfy design rules. Experimental results show that the proposed technique is able to generate wells close to manual designs with comparable post-layout circuit performance.