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Ingeniería de Computadores Computación Sistemas de Información Tecnologías de la información

Companies and scientists working in areas such as finance or genomics are generating enormously large datasets (in the order of petabytes) commonly referred as Big Data. How to efficiently and effectively process such large amounts of data is an open research problem. Since communication is involved in Big Data processing at many levels, at the NaNoNetworking Center in Catalunya (N3Cat) we are currently investigating the potential role of wireless communications in the Big Data scenario. The main focus of the project is to evaluate the impact of applying wireless communications and networking methods to processors and data centers oriented to the management of Big Data. OBJECTIVES =========== N3Cat is looking for students wanting to work in the area of wireless communications for Big Data. To this end, the candidate will work on one of the following areas: - Traffic analysis of Big Data frameworks and applications, as well as in smaller manycore systems. - Channel characterization in Big Data environments: indoor, within the racks of a data center, within the package of CPU, within a chip. - Design of wireless communication protocols for computing systems from the processor level to the data center level.

Ingeniería de Computadores Computación

Machine Learning (ML) has taken the world by storm and has become a fundamental pillar of engineering. As a result, the last decade has witnessed an explosive growth in the use of deep neural networks (DNNs) in pursuit of exploiting the advantages of ML in virtually every aspect of our lives: computer vision, natural language processing, medicine or economics are just a few examples. However, NOT all DNNs fit to all problems: convolutional NNs are good for computer vision, recurrent NNs are good for temporal analysis, and so on. In this context, the main focus of N3Cat and BNN-UPC is to explore the possibilities of the new and less explored variant called Graph Neural Networks (GNNs), whose aim is to learn and model graph-structured data. This has huge implications in fields such as quantum chemistry, computer networks, or social networks among others. OBJECTIVES =========== N3Cat and BNN-UPC are looking for students wanting to work in the area of Graph Neural Networks studying their uses, processing architectures, and algorithms. To this end, the candidate will work on ONE of the following areas: - Investigating the state of the art on this area, surveying the different works done in terms of applications, processing frameworks, algorithms, benchmarks, datasets. This can be taken from a hardware or software perspective. - Helping to build a testbed formed by a cluster of GPUs that will be running pyTorch or Tensorflow. We will instrument the testbed to measure the computation workload and communication flows between GPUs. - Analyzing the communication workload of running a GNN either in the testbed or by means of architectural simulations. - Developing means of accelerating GNN processing in software (e.g., improving scheduling of the message passing) or hardware (e.g. designing a domain-specific architecture).

Ingeniería de Computadores Computación Tecnologías de la información

Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, a quantum computer faces many challenges relative to the movement of qubits which is completely different than the movement of classical data. This thesis delves into these challenges and proposes solutions to create scalable quantum computers

Computación

Computación Tecnologías de la información

UPC and Nestlé are offering a new position to develop the TFG in the field of Machine Learning and Cybersecurity. This TFG will be fully funded (internship) and carried out in collaboration with the Global Security Operations Center of Nestlé and UPC.

Ingeniería de Computadores Computación Sistemas de Información Tecnologías de la información

Artificial Intelligence (AI) software is indisputable the key competitive advantage in every new computing system embedded in cars, planes, trains, and the like, providing a reach set of functionalities. AI-software in those systems must perform real-time processing of a massive amount of data coming from sensors like cameras and LiDARs. This requires the use of complex MPSoCs (Multi-Processor System on Chip) as main computing platform. However, software's execution time when running on MPSoCs is hard to model accurately due to its complexity encompassing high-performance

Ingeniería de Computadores Computación Sistemas de Información Tecnologías de la información

Processor design is being democratized these days and even small companies and research centers can design and fabricate their test chips. This transition from a proprietary-only approach controlled by Intel, AMD, Arm, etc. to an open and collaborative environment, has been fostered by the emergence of the open RISC-V Instruction Set Architecture (ISA). Oppositely to x86 and ARM Instruction Set Architectures (ISAs), RISC-V emerges as an open source alternative attracting high attention from industry worldwide, but particularly European safety-relevant industry (automotive,

Computación

Weathering model for the simulation and visualization of lichens in 3D models of cultural heritage.

Computación

Amb el present projecte es pretén visualitzar, en temps real, dades procedents de simulacions CFD. Aquestes dades es troben emmagatzemades en fitxers que són el resultat de simular fenòmens físics com foc, fum o vent al llarg d'un interval de temps. El projecte consisteix en desenvolupar una eina que permeti visualitzar l'estat del fenòmen per cada instant de temps utilitzant algorismes de visualització de dades volumètriques.

Ingeniería de Computadores Computación Sistemas de Información Ingeniería del Software Tecnologías de la información

The last decade has witnessed a sudden breakthrough of Artificial Intelligence (AI) in the Automotive market. AI solutions have been increasingly deployed to provide advanced driving assistance solutions or even enable autonomous operation. Pedestrian detection, unintentional lane crossing monitoring, and Tesla AutoPilot are just few examples of this trend. In this TFG we will work in the area of performance modeling and schedule optimization for AI-Based autonomous driving (AD) systems, to analyze the execution of autonomous driving functions.

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