Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo

El diseño y fabricación de hardware es costoso, tanto en tiempo como en inversión económica, razón por la que los circuitos integrados se fabrican siempre en gran volumen, para aprovechar la economía de escala. Por esa razón la mayoría de procesadores fabricados son de propósito general, ampliando así su campo de aplicaciones. En los últimos años, sin embargo, cada vez se fabrican más procesadores para aplicaciones específicas, entre ellos aquellos destinados a acelerar el trabajo con redes neuronales profundas. Este artículo introduce la necesidad de este tipo de hardware especializado, describiendo su finalidad, funcionamiento e implementaciones actuales.The design and manufacture of hardware is expensive, both in time and in economic investment, which is why integrated circuits are always manufactured in large volume, to take advantage of economies of scale. For this reason, the majority of processors manufactured are general purpose, thus expanding its range of applications. In recent years, however, more and more processors are being manufactured for specific applications, including those aimed at accelerating work with deep neural networks. This article introduces the need for this type of specialized hardware, describing its purpose, operation and current implementations.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadore

Mejoras en tratamiento de problemas de clasificación con modelos basados en autoencoders

In summary, the main contributions of this thesis are as follows: A theoretical analysis and taxonomy of the main autoencoder variants present in the literature, composing a guide to ease their selection and use. A complete software package which automatizes a great part of the implementation work for autoencoders and simplifies its use to a level similar to other feature extraction methods. A synthesis and organization work of the peculiarities that supervised learning problems can present when data points are represented in a nonstandard fashion. A demonstration of the diverse applications of autoencoder-based models, identifying and exposing several strategies to solve unsupervised problems by means of variable transformations. Three new models, Scorer, Skaler and Slicer, focused on data complexity reduction in classification problems. This document introduces all global concepts needed to understand the published articles and provides a theoretical vision of the representation learning problem and of the deep learning tool set, which includes the main object of study. In addition, it explains the techniques that help put into practice these models and how they execute on computation infrastructures. Next, the material published throughout the doctoral period is introduced and five articles published in renowned journals are reproduced. Finally, these and other activities carried out are summarized and the lines of work that would continue the achieved advancements are presented.En resumen, las principales contribuciones de la tesis son las siguientes: Un análisis teórico y taxonomía de las principales variantes de autoencoders presentes en la literatura, componiendo una guía para facilitar la selección y el uso de las mismas. Un completo paquete software que automatiza gran parte del trabajo de implementación de autoencoders y acerca su uso a un nivel comparable al de otros métodos de extracción de características más simples. Un trabajo de organización y síntesis de las particularidades que pueden presentar los problemas de aprendizaje supervisado cuando los datos están representados de formas no estándares. Una demostración de las diversas aplicaciones de los modelos basados en autoencoders, identificando y exponiendo distintas estrategias para resolver problemas no supervisados mediante manipulación de las variables. Tres nuevos modelos, Scorer, Skaler y Slicer, enfocados a la reducción de la complejidad de datos en problemas de clasificación. El presente documento introduce todos los conceptos globales necesarios para entender los artículos publicados y aporta una visión teórica de la problemática del aprendizaje de representaciones y del conjunto de herramientas de aprendizaje profundo, dentro del cual se enmarca el objeto principal de estudio. Además, se explican las técnicas que ayudan a llevar a la práctica estos modelos y cómo se ejecutan sobre las infraestructuras de computación. Posteriormente se introduce el material publicado a lo largo del periodo doctoral y se reproducen cinco artículos publicados en revistas científicas de notable reputación. Finalmente se resumen estas y otras actividades llevadas a cabo, y se presentan las líneas de trabajo que continuarían con los avances ya realizados.Tesis Univ. Granada.Predoctoral program Formación del Profesorado Universitario (ref. FPU17/04069) from the Spanish Ministry of Universitie

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

This publication is supported by ArcelorMittal, Luxembourg Global R&D, specifically the project granted by ArcelorMittal Global R&D Digital Portfolio in collaboration with the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) , University of Granada. This publication is supported by the Andalusian Excel-lence, Spain project P18-FR-4961 and SOMM17/6110/UGR. D. Charte is supported by the Spanish Ministry of Universities, Spain under the FPU program (Ref. FPU17/04069) . Funding for open access charge: Universidad de Granada/CBUA.Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.ArcelorMittal, Luxembourg Global RDArcelorMittal Global R&D Digital Portfolio in collaboration with the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of GranadaAndalusian Excellence, Spain project P18-FR-4961 SOMM17/6110/UGRSpanish Ministry of Universities, Spain under the FPU program FPU17/04069Universidad de Granada/CBU

Arti cial intelligence within the interplay between natural and arti cial Computation: advances in data science, trends and applications

Arti cial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and arti cial intelligence within the interplay between natural and arti cial computation. A review of recent works published in the latter eld and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in arti cial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application elds covered in the essay, from theoretical models in arti cial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.Ministry of Science and Innovation, Spain (MICINN) Spanish Government TIN2017-85827-P RTI2018-098913-B-I00 PSI2015-65848-R PGC2018-098813-B-C31 PGC2018-098813-B-C32 RTI2018-101114-B-I TIN2017-90135-R RTI2018-098743-B-I00 RTI2018-094645-B-I00Autonomous Government of Andalusia (Spain) UMA18-FEDERJA-084Conselleria de Cultura, Educacion e Ordenacion Universitaria of Galicia ED431C2017/12 ED431G/08 ED431C2018/29 Y2018/EMT-5062 ED431F2018/02Michael J. Fox Foundation for Parkinson's ResearchAbbott LaboratoriesBiogenF. Hoffman-La Roche Ltd.General Electric GE HealthcareRoche HoldingGenentechPfizerUnited States Department of Health & Human Services National Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) U01 AG024904DOD ADNI (Department of Defense) W81XWH-12-2-0012United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute on Aging (NIA) United States Department of Health & Human Services National Institutes of Health (NIH) - USANIH National Institute of Biomedical Imaging & Bioengineering (NIBIB)AbbVieAlzheimer's Association Alzheimer's Drug Discovery FoundationAraclon BiotechBioClinica, Inc. BiogenBristol-Myers SquibbCereSpir, Inc.Eisai Co LtdElan Pharmaceuticals, Inc.Eli LillyEuroImmunF. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.FujirebioIXICO Ltd.Janssen Alzheimer Immunotherapy Research AMP; Development, LLC.Johnson AMP; Johnson Pharmaceutical Research AMP; Development LLC.LumosityLundbeck CorporationMerck & CompanyMeso Scale Diagnostics, LLC.NeuroRx Research Neurotrack TechnologiesNovartisPiramal ImagingServierTakeda Pharmaceutical Company LtdTransition TherapeuticsCanadian Institutes of Health Research (CIHR)Spanish Government FPU15/06512 FPU17/04154 FJCI-2017-3302