Marina Zapater Sancho

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Real-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers' infrastructures in current multi-user environments. High Efficiency Video Coding (HEVC) makes efficient online trans ...

Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers ...

Deeply-scaled three-dimensional (3D) Multi-Processor Systems-on-Chip (MPSoCs) enable high performance and massive communication bandwidth for next-generation computing. However, as process nodes shrink, temperature-dependent leakage dramatically increases, ...

The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit custom ...

In order to optimize the energy use of servers in Data Centers, techniques such as power capping or power budgeting are usually deployed. These techniques rely on the prediction of the power and execution time of applications. These data are obtained via d ...

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications’ run-time. ...

Next generation workloads, such as genome sequencing, have an astounding impact in the healthcare sector. Sequence alignment, the first step in genome sequencing, has experienced recent breakthroughs, which resulted in next generation sequencing (NGS). As ...

With the spread of cloud services and Internet of Things concept, there is a popularization of machine learning and artificial intelligence based analytics in our everyday life. However, an efficient deployment of these data-intensive services requires per ...

2.5D Systems-on-Package (SoPs) are composed by several chiplets placed on an interposer. They are becoming increasingly popular as they enable easy integration of electronic components in the same package and high fabrication yields. Nevertheless, they int ...

The increasing demand for computing power and the emergence of heterogeneous computing architectures have driven the exploration of innovative techniques to address current limitations in both the compute and memory subsystems. One such solution is the use ...

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques suc ...

The increasing ubiquity of edge devices in the consumer market, along with their ever more computationally expensive workloads, necessitate corresponding increases in computing power to support such workloads. In-memory computing is attractive in edge devi ...

The need for efficient Convolutional Neural Network (CNNs) targeting embedded systems led to the popularization of Binary Neural Networks (BNNs), which significantly reduce execution time and memory requirements by representing the operands using only one ...

The power density and, consequently, power hungriness of server processors is growing by the day. Traditional air cooling systems fail to cope with such high heat densities, whereas single-phase liquid-cooling still requires high mass flowrate, high pumpin ...

Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the ...

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

Integrated Flow Cell Array (FCA) technology promises to address the power delivery and heat dissipation challenges in three-dimensional Multi Processor Systems-on-Chips (3D MPSoCs) by providing combined inter-tier liquid cooling and power generation capabi ...

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource m ...

Marina Zapater Sancho

Chat with Graph Search

MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video Transcoding

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Towards Deeply Scaled 3D MPSoCs with Integrated Flow Cell Array Technology

Exploring manycore architectures for next-generation HPC systems through the MANGO approach

Fast Energy Estimation Through Partial Execution of HPC Applications

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

Genome Sequence Alignment - Design Space Exploration for Optimal Performance and Energy Architectures

An Associativity-Agnostic in-Cache Computing Architecture Optimized for Multiplication

REMOTE: Re-thinking Task Mapping on Wireless 2.5D Systems-on-Package for Hotspot Removal

Intermediate Address Space: virtual memory optimization of heterogeneous architectures for cache-resident workloads

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

Towards Near-Threshold Server Processors

BLADE: A BitLine Accelerator for Devices on the Edge

Accelerating Inference on Binary Neural Networks with Digital RRAM Processing

Enhancing Two-Phase Cooling Efficiency through Thermal- Aware Workload Mapping for Power-Hungry Servers

Predictive Reliability and Fault Management in Exascale Systems

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

A Design Framework for Thermal-Aware Power Delivery Network in 3D MPSoCs with Integrated Flow Cell Arrays

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers