HIGH-PERFORMANCE SPECTRAL METHODS FOR COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS

Recent research shows that by leveraging the key spectral properties of eigenvalues and eigenvectors of graph Laplacians, more efficient algorithms can be developed for tackling many graph-related computing tasks. In this dissertation, spectral methods are utilized for achieving faster algorithms in the applications of very-large-scale integration (VLSI) computer-aided design (CAD) First, a scalable algorithmic framework is proposed for effective-resistance preserving spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving the key spectral (structural) properties of the original graph. Our framework is built upon the following three key components: a spectrum-preserving node aggregation and reduction scheme, a spectral graph sparsification framework with iterative edge weight scaling, as well as effective-resistance preserving post-scaling and iterative solution refinement schemes. We show that the resultant spectrally-reduced graphs can robustly preserve the first few nontrivial eigenvalues and eigenvectors of the original graph Laplacian and thus allow for developing highly-scalable spectral graph partitioning and circuit simulation algorithms. Based on the framework of the spectral graph reduction, a Sparsified graph-theoretic Algebraic Multigrid (SAMG) is proposed for solving large Symmetric Diagonally Dominant (SDD) matrices. The proposed SAMG framework allows efficient construction of nearly-linear sized graph Laplacians for coarse-level problems while maintaining good spectral approximation during the AMG setup phase by leveraging a scalable spectral graph sparsification engine. Our experimental results show that the proposed method can offer more scalable performance than existing graph-theoretic AMG solvers for solving large SDD matrices in integrated circuit (IC) simulations, 3D-IC thermal analysis, image processing, finite element analysis as well as data mining and machine learning applications. Finally, the spectral methods are applied to power grid and thermal integrity verification applications. This dissertation introduces a vectorless power grid and thermal integrity verification framework that allows computing worst-case voltage drop or thermal profiles across the entire chip under a set of local and global workload (power density) constraints. To address the computational challenges introduced by the large 3D mesh-structured thermal grids, we apply the spectral graph reduction approach for highly-scalable vectorless thermal (or power grids) verification of large chip designs. The effectiveness and efficiency of our approach have been demonstrated through extensive experiments

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Análisis de datos de citometría de flujo mediante el uso de domain-adversarial autoencoders

Trabajo fin de máster en Bioinformática y Biología ComputacionalMachine Learning is a field of Artificial Intelligence focused on automatic data analysis. In the era of big data, there appear algorithms that allow the analysis of large quantities of data efficiently, incorporating more knowledge into our studies. One of the main fields of application for these algorithms is bioinformatics, where large amounts of high-dimensional data are typically analyzed. However, one of the main difficulties in the automatic analysis of data with a biological origin is the inevitable variation that occurs in the experimental conditions, causing the well-known batch effects. This makes it difficult to integrate data that come from different experimental sources, thus reducing the simultaneous capacity for analysis and losing relevant biological information. Focused on flow cytometry data, in this work we propose a new algorithm in the context of unsupervised learning with the aim of smoothing the influence of batch effects simultaneously under an arbitrary number of experimental conditions. Applying state-of-the-art techniques in Machine Learning, such as domain adaptation and adversarial learning, we present the domainadversarial autoencoder (DAE). For the validation of the DAE as a domain adaptation or batch normalization algorithm, in this work we carry out experiments with three data sets. The first two are simple, artificial datasets composed of beads that have been passed through the cytometer in a controlled environment. In one of them, the clogging or misalignment of the cytometer is artificially simulated. In the other, we have the same data analyzed on two different machines. The third example is a real dataset with dendritic cells of mice that have also been collected on two different cytometers. Firstly, we show how these batch effects influence the analysis typically applied by flow cytometry users, such as clustering with Phenograph or visualization with t-SNE. Secondly, we see how the DAE manages to efficiently alleviate the batch effects in these examples and improve the clustering results, achieving a notable increase in the F1-score after the correction. In addition, we provide with a visual evaluation of the representations in two-dimensional spaces learnt with a standard autoencoder (SAE), t-SNE and a DAE. Additionally, in this work we present a novel method to evaluate the quality of the batch normalization of data using statistical distances. In particular, we use the multidimensional version of the Kolmogorov-Smirnov distance between distributions. We show that the distribution of the data in the latent representation of the DAE is very similar when the data comes from different experiments, presenting a smaller distance than in the case of the SAE, where we do not provide the algorithm with domain information in the training step. Therefore, this work allows us to conclude that domain adaptation in flow cytometry data opens a new line of research, which is focused in developing tools for the integration of data from different experiment