Programa de Doctorat en Arquitectura de Computadors: Envíos recientes
Mostrando ítems 61-80 de 260
On scalable, reconfigurable, and intelligent metasurfaces
Sixth Generation (6G) of wireless networks will be even more heterogeneous and dense as compared to Fifth Generation (5G) and other legacy networks. Thus, the 6G architecture will need to be adapted to serve the ever-evolving ...
Next generation overlay networks : security, trust, and deployment challenges
Overlay networks are a technique to build a new network on top of an existing one. They are a key tool to add functionality to existing networks, and are used in different layers of the Internet stack for a wide variety ...
Breaking host-centric management of task-based parallel programming models
Heterogeneous platforms had become popular to increase the computational power of the systems within a constrained power budget. They are present in several systems, from embedded platforms and mobile devices to high-end ...
Novel techniques to improve the performance and the energy of vector architectures
The rate of annual data generation grows exponentially. At the same time, there is a high demand to analyze that information quickly. In the past, every processor generation came with a substantial frequency increase, ...
Scheduling and resource management solutions for the scalable and efficient design of today's and tomorrow's HPC machines
In recent years, high-performance computing research became essential in pushing the boundaries of what men can know, predict, achieve, and understand in the experimented reality. HPC Workloads grow in size and complexity ...
Image and video object segmentation in low supervision scenarios
Computer vision plays a key role in Artificial Intelligence because of the rich semantic information contained in pixels and the ubiquity of cameras nowadays. Multimedia content is on a rise since social networks have such ...
On the design and development of programming models for exascale systems
High Performance Computing (HPC) systems have been evolving over time to adapt to the scientific community requirements. We are currently approaching to the Exascale era. Exascale systems will incorporate a large number ...
Learning workload behaviour models from monitored time-series for resource estimation towards data center optimization
In recent years there has been an extraordinary growth of the demand of Cloud Computing resources executed in Data Centers. Modern Data Centers are complex systems that need management. As distributed computing systems ...
Energy-efficient architectures for recurrent neural networks
Deep Learning algorithms have been remarkably successful in applications such as Automatic Speech Recognition and Machine Translation. Thus, these kinds of applications are ubiquitous in our lives and are found in a plethora ...
High-performance and energy-efficient irregular graph processing on GPU architectures
Graph processing is an established and prominent domain that is the foundation of new emerging applications in areas such as Data Analytics and Machine Learning, empowering applications such as road navigation, social ...
Non-functional considerations of time-randomized processor architectures
Critical Real-Time Embedded Systems (CRTES) are the subset of embedded systems with timing constraints whose miss behavior can endanger human lives or expensive equipment. To provide evidence of correctness, CRTES are ...
Definition of new WAN paradigms enabled by smart measurements
Nowadays massive amounts of data are being moved over the Internet thanks to data-hungry applications, Big Data, and multimedia content. Combined with a reduction in cost and augmented reliability for high-speed broadband ...
Runtime-assisted coherent caching
In the middle of the 2000s a fundamental change of course occurred in computer architecture because techniques such as frequency scaling and instruction level parallelism were providing rapidly diminishing returns. Since ...
Deep learning that scales: leveraging compute and data
Deep learning has revolutionized the field of artificial intelligence in the past decade. Although the development of these techniques spans over several years, the recent advent of deep learning is explained by an increased ...
Runtime-assisted optimizations in the on-chip memory hierarchy
Following Moore's Law, the number of transistors on chip has been increasing exponentially, which has led to the increasing complexity of modern processors. As a result, the efficient programming of such systems has become ...
Low-power accelerators for cognitive computing
Deep Neural Networks (DNNs) have achieved tremendous success for cognitive applications, and are especially efficient in classification and decision making problems such as speech recognition or machine translation. Mobile ...
Adaptive learning-based resource management strategy in fog-to-cloud
Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ...
Mobility-aware mechanisms for fog node discovery and selection
The recent development of delay-sensitive applications has led to the emergence of the fog computing paradigm. Within this paradigm, computation nodes present at the edge of the network can act as fog nodes (FNs) capable ...
Programming models to support data science workflows
Data Science workflows have become a must to progress in many scientific areas such as life, health, and earth sciences. In contrast to traditional HPC workflows, they are more heterogeneous; combining binary executions, ...
Enabling knowledge-defined networks : deep reinforcement learning, graph neural networks and network analytics
Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new ...
