Recent submissions

Multi-agent graph learning-based optimization and its applications to computer networks

Bernárdez Gil, Guillermo (Date of defense: 2024-03-15)

(English) In the wake of a digital revolution, contemporary society finds itself entrenched in an era where network applications' demands surpass the capabilities of conventional network management solutions. This dissertation ...

Smart and efficient sensor networks operation for 5G and beyond ecosystems

El Sayed, Ahmad Mohammad (Date of defense: 2024-01-30)

(English) Sensor Networks (SN) will play an integral role in Beyond 5G (B5G) ecosystems, especially for highly-distributed use cases and services such as Digital Twins (DT). Thus, the underlying transport network needs to ...

A double full-stack architecture for multi-core quantum computers

Rodrigo Muñoz, Santiago (Date of defense: 2023-12-18)

(English) Despite its tremendous potential, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Continued progress in the fabrication and control of qubits ...

Acceleration of automatic speech recognition for low-power devices

Pinto Rivero, Dennis (Date of defense: 2022-11-09)

(English) In this thesis, we study the challenges preventing ASR deployment on edge devices and propose innovations to tackle them, hopefully moving the technology a step forward to the future. First, we characterize ...

Efficient hardware acceleration of deep neural networks via arithmetic complexity reduction

Reggiani, Enrico (Date of defense: 2023-10-26)

(English) Over the past decade, significant progresses in the field of artificial intelligence have led to remarkable advancements in a wide range of technologies. Deep learning, a subfield of machine learning centered ...

Job scheduling for disaggregated memory in high performance computing systems

Vieira Zacarias, Felippe (Date of defense: 2023-10-09)

(English) In a typical HPC cluster system, a node is the elemental component unit of this architecture. Memory and compute resources are tightly coupled in each node and the rigid boundaries between nodes limits compute ...

Adapting floating-point precision to accelerate deep neural network training

Osorio Ríos, John Haiber (Date of defense: 2023-10-18)

(English) Deep Neural Networks (DNNs) have become ubiquitous in a wide range of application domains. Despite their success, training DNNs is an expensive task which has motivated the use of reduced numerical precision ...

Deep learning for spatio-temporal forecasting: benchmarks, methods, and insights from mobility and weather predictions

Herruzo Sánchez, Pedro (Date of defense: 2023-10-30)

(English) This thesis explores the intersection of deep learning and spatio-temporal forecasting, focusing on the challenges and opportunities present in applying machine learning methods to predict complex geospatial and ...

Advanced hardware prefetching in virtual memory systems

Vavouliotis, Georgios (Date of defense: 2023-09-12)

(English) Despite groundbreaking technological innovations, the disparity between processor and memory speeds (known as Memory Wall) is still a major performance obstacle for modern systems. Hardware prefetching is a ...

Convergence of high performance computing, big data, and machine learning applications on containerized infrastructures

Liu, Peini (Date of defense: 2023-07-17)

(English) The convergence of High Performance Computing (HPC), Big Data (BD), and Machine Learning (ML) in the computing continuum is being pursued in earnest across the academic and industry. We envision virtualization ...

Software diagnostics for autonomous safety-critical control-systems based on artificial intelligence

Fernández Muñoz, Javier (Date of defense: 2023-07-18)

(English) Machine Learning (ML) systems allow the efficient implementation of functionalities that can be hard to program by traditional software due to the high spectrum of inputs that hinder the definition of a specific ...

Scaling deep learning workloads. Applications in computer vision and seismology

Cruz de la Cruz, Stalin Leonel (Date of defense: 2023-07-06)

(English) Deep learning techniques have an enormous impact on the state-of-the-art in many fields, such as computer vision, natural language processing, audio analysis and synthesis, and many others. The increasing computing ...

Hardware/software solutions to enable the use of high-performance processors in the most stringent safety-critical systems

Alcaide Portet, Sergi (Date of defense: 2023-07-19)

(English) Future Safety-Critical Systems require a boost in guaranteed performance in order to satisfy the increasing performance demands of the state-of-the-art complex software features. Ar1 approach to achieve these ...

Programa de Doctorat en Arquitectura de Computadors: Recent submissions

Multi-agent graph learning-based optimization and its applications to computer networks

Smart and efficient sensor networks operation for 5G and beyond ecosystems

A double full-stack architecture for multi-core quantum computers

Acceleration of automatic speech recognition for low-power devices

Efficient hardware acceleration of deep neural networks via arithmetic complexity reduction

Job scheduling for disaggregated memory in high performance computing systems

Adapting floating-point precision to accelerate deep neural network training

Deep learning for spatio-temporal forecasting: benchmarks, methods, and insights from mobility and weather predictions

Advanced hardware prefetching in virtual memory systems

Convergence of high performance computing, big data, and machine learning applications on containerized infrastructures

Software diagnostics for autonomous safety-critical control-systems based on artificial intelligence

Scaling deep learning workloads. Applications in computer vision and seismology

Hardware/software solutions to enable the use of high-performance processors in the most stringent safety-critical systems

Leveraging graph neural networks for optimization and traffic compression in network digital twins

Artificial intelligence solutions for quantum communications

On the data quality improvement of air pollution monitoring low-cost sensor networks using data-driven techniques

Practical strategies to monitor and control contention in shared resources of critical real-time embedded systems

Modelling and predicting extreme behavior in critical real-time systems with advanced statistics

Techniques for efficient and secure optical networks

Distributed cloud-edge analytics and machine learning for transportation emissions estimation

Programa de Doctorat en Arquitectura de Computadors: Recent submissions

Multi-agent graph learning-based optimization and its applications to computer networks ﻿

Smart and efficient sensor networks operation for 5G and beyond ecosystems ﻿

A double full-stack architecture for multi-core quantum computers ﻿

Acceleration of automatic speech recognition for low-power devices ﻿

Efficient hardware acceleration of deep neural networks via arithmetic complexity reduction ﻿

Job scheduling for disaggregated memory in high performance computing systems ﻿

Adapting floating-point precision to accelerate deep neural network training ﻿

Deep learning for spatio-temporal forecasting: benchmarks, methods, and insights from mobility and weather predictions ﻿

Advanced hardware prefetching in virtual memory systems ﻿

Convergence of high performance computing, big data, and machine learning applications on containerized infrastructures ﻿

Software diagnostics for autonomous safety-critical control-systems based on artificial intelligence ﻿

Scaling deep learning workloads. Applications in computer vision and seismology ﻿

Hardware/software solutions to enable the use of high-performance processors in the most stringent safety-critical systems ﻿

Leveraging graph neural networks for optimization and traffic compression in network digital twins ﻿

Artificial intelligence solutions for quantum communications ﻿

On the data quality improvement of air pollution monitoring low-cost sensor networks using data-driven techniques ﻿

Practical strategies to monitor and control contention in shared resources of critical real-time embedded systems ﻿

Modelling and predicting extreme behavior in critical real-time systems with advanced statistics ﻿

Techniques for efficient and secure optical networks ﻿

Distributed cloud-edge analytics and machine learning for transportation emissions estimation ﻿

Multi-agent graph learning-based optimization and its applications to computer networks

Smart and efficient sensor networks operation for 5G and beyond ecosystems

A double full-stack architecture for multi-core quantum computers

Acceleration of automatic speech recognition for low-power devices

Efficient hardware acceleration of deep neural networks via arithmetic complexity reduction

Job scheduling for disaggregated memory in high performance computing systems

Adapting floating-point precision to accelerate deep neural network training

Deep learning for spatio-temporal forecasting: benchmarks, methods, and insights from mobility and weather predictions

Advanced hardware prefetching in virtual memory systems

Convergence of high performance computing, big data, and machine learning applications on containerized infrastructures

Software diagnostics for autonomous safety-critical control-systems based on artificial intelligence

Scaling deep learning workloads. Applications in computer vision and seismology

Hardware/software solutions to enable the use of high-performance processors in the most stringent safety-critical systems

Leveraging graph neural networks for optimization and traffic compression in network digital twins

Artificial intelligence solutions for quantum communications

On the data quality improvement of air pollution monitoring low-cost sensor networks using data-driven techniques

Practical strategies to monitor and control contention in shared resources of critical real-time embedded systems

Modelling and predicting extreme behavior in critical real-time systems with advanced statistics

Techniques for efficient and secure optical networks

Distributed cloud-edge analytics and machine learning for transportation emissions estimation