The physical design process of large-scale designs is a time-consuming task, often requiring hours to days to complete, with routing being the most critical and complex step. As the the complexity of Integrated Circuits (ICs) increases, there is an increased demand for accurate routing quality prediction. Accurate congestion prediction aids in identifying design flaws early on, thereby accelerating circuit design and conserving resources. Despite the advancements in current congestion prediction methodologies, an essential aspect that has been largely overlooked is the spatial label-correlation between different grids in congestion prediction. The spatial label-correlation is a fundamental characteristic of circuit design, where the congestion status of a grid is not isolated but inherently influenced by the conditions of its neighboring grids. In order to fully exploit the inherent spatial label-correlation between neighboring grids, we propose a novel approach, {\ours}, i.e., VAriational Label-Correlation Enhancement for Congestion Prediction, which considers the local label-correlation in the congestion map, associating the estimated congestion value of each grid with a local label-correlation weight influenced by its surrounding grids. {\ours} leverages variational inference techniques to estimate this weight, thereby enhancing the regression model's performance by incorporating spatial dependencies. Experiment results validate the superior effectiveness of {\ours} on the public available \texttt{ISPD2011} and \texttt{DAC2012} benchmarks using the superblue circuit line.
Fast and efficient speech enhancement with variational autoencoders
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学及其应用实验室）
基于变分自编码器的无监督语音增强方法与常用的有监督方法相比具有良好的性能。该方法涉及使用预训练的深度语音先验以及参数噪声模型，其中噪声参数是利用基于期望最大化（EM）的方法从有噪语音信号中学习的。E步涉及一个棘手的潜在后验分布。解决这一步的现有算法要么是基于计算量大的Monte Carlo Markov Chain抽样方法和变分推理，要么是基于低效的优化方法。在本文中，我们提出了一种基于Langevin动力学的新方法，该方法生成多个样本序列，并带有一个基于总变分的正则化，以结合潜在向量的时间相关性。我们的实验表明，开发的框架，使计算效率和增强质量之间的有效折衷，并优于现有的方法。
