Programa de Doctorat en Arquitectura de Computadors: Envíos recientes
Mostrando ítems 81-100 de 272
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 ...
Exploiting frame coherence in real-time rendering for energy-efficient GPUs
The computation capabilities of mobile GPUs have greatly evolved in the last generations, allowing real-time rendering of realistic scenes. However, the desire for processing complex environments clashes with the ...
Security strategies in genomic files
There are new mechanisms to sequence and process the genomic code, discovering thus diagnostic tools and treatments. The file for a sequenced genome can reach hundreds of gigabytes. Thus, for further studies, we need new ...
Virtualization techniques for memory resource exploitation
Cloud infrastructures have become indispensable in our daily lives with the rise of cloud-based services offered by companies like Facebook, Google, Amazon and many others. These cloud infrastructures use a large numbers ...
Exploring the topical structure of short text through probability models : from tasks to fundamentals
Recent technological advances have radically changed the way we communicate. Today’s communication has become ubiquitous and it has fostered the need for information that is easier to create, spread and consume. As a ...
An extensive study on iterative solver resilience : characterization, detection and prediction
Soft errors caused by transient bit flips have the potential to significantly impactan applicalion's behavior. This has motivated the design of an array of techniques to detect, isolate, and correct soft errors using ...
Ambient intelligence in buildings : design and development of an interoperable Internet of Things platform
During many years, people and governments have been warned about the increasing levels of pollution and greenhouse gases (GHG) emissions that are endangering our lives on this planet. The Information and Communication ...
On the limits of probabilistic timing analysis
Over the last years, we are witnessing the steady and rapid growth of Critica! Real-Time Embedded Systems (CRTES) industries, such as automotive and aerospace. Many of the increasingly-complex CRTES' functionalities that ...
Improving decision tree and neural network learning for evolving data-streams
High-throughput real-time Big Data stream processing requires fast incremental algorithms that keep models consistent with most recent data. In this scenario, Hoeffding Trees are considered the state-of-the-art single ...
