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Marina Zapater Sancho

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Publications associées (59)

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

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Darong Huang, Luis Maria Costero Valero

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 ...

2024

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

Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Halima Najibi

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 ...

2019

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

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Kosar Haghshenas

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 ...

2022

COCKTAIL: Multi-Core Co-Optimization Framework With Proactive Reliability Management

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Darong Huang

High performance computing (HPC) servers aim to meet an increase in the number and complexity of tasks and, consequently, to address the energy efficiency challenge. In addition to energy efficiency, it is essential to manage lifetime limitations of power- ...

2022

A Hybrid Cache HW/SW Stack for Optimizing Neural Network Runtime, Power and Endurance

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, William Andrew Simon

Hybrid caches consisting of both SRAM and emerging Non-Volatile Random Access Memory (eNVRAM) bitcells increase cache capacity and reduce power consumption by taking advantage of eNVRAM's small area footprint and low leakage energy. However, they also inhe ...

2020

Gem5-X: A Gem5-Based System Level Simulation Framework to Optimize Many-Core Platforms

David Atienza Alonso, Marina Zapater Sancho, William Andrew Simon, Yasir Mahmood Qureshi

The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this p ...

IEEE2019

Containergy-A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar

Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and ...

2020

Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Leila Cammoun, Marina Zapater Sancho, Tomas Teijeiro Campo, Valentin Alexandre Guy Gabeff

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

2021

Design of a Two-Phase Gravity-Driven Micro-Scale Thermosyphon Cooling System for High-Performance Computing Data Centers

John Richard Thome, David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, André Olivier Seuret

Next-generation High-Performance Computing (HPC) systems need to provide outstanding performance with unprecedented energy efficiency while maintaining servers at safe thermal conditions. Air cooling presents important limitations when employed in HPC infr ...

2018

A Fast, Reliable and Wide-voltage-range In-memory Computing Architecture

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, William Andrew Simon

In-Memory Computing (IMC) solutions, and particularly bitline computing in SRAM, appear promising as they mitigate one of the most energy consuming aspects in computation: data movement. In this work we propose a fast (2.4Ghz for bitwise operations and 2.4 ...

Association for Computing Machinery2019

System-Level Exploration of In-Package Wireless Communication for Multi-Chiplet Platforms

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Joshua Alexander Harrison Klein, Rafael Medina Morillas

Multi-Chiplet architectures are being increasingly adopted to support the design of very large systems in a single package, facilitating the integration of heterogeneous components and improving manufacturing yield. However, chiplet-based solutions have to ...

2023

Thermal and Power-Aware Run-Time Performance Management of 3D MPSoCs with Integrated Flow Cell Arrays

David Atienza Alonso, Marina Zapater Sancho, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Halima Najibi

Flow Cell Arrays (FCA) technology employs microchannels filled with an electrolytic fluid to concurrently provide cooling and power generation to integrated circuits (ICs). This solution is particularly appealing for Three-Dimensional Multi-Processor Syste ...

2022

Gem5-X : A Many-Core Heterogeneous Simulation Platform for Architectural Exploration and Optimization

David Atienza Alonso, Marina Zapater Sancho, William Andrew Simon, Yasir Mahmood Qureshi

The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this paper, ...

2021

A QoS and Container-Based Approach for Energy Saving and Performance Profiling in Multi-Core Servers

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar

In this work we present ContainEnergy, a new performance evaluation and profiling tool that uses software containers to perform application runtime assessment, providing energy and performance profiling data. It is focused on energy efficiency for next gen ...

IEEE2019

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Irem Boybat Kara, Flavio Ponzina, Joshua Alexander Harrison Klein, Yuxuan Wang, Abu Sebastian

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Luis Maria Costero Valero

The advent of online video streaming services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher quality and compression at the cost of increased complexity. On one hand, HEVC exposes a ...

2020

Exact Neural Networks from Inexact Multipliers via Fibonacci Weight Encoding

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, William Andrew Simon

Edge devices must support computationally demanding algorithms, such as neural networks, within tight area/energy budgets. While approximate computing may alleviate these constraints, limiting induced errors remains an open challenge. In this paper, we pro ...

2021

Towards Accurate RISC-V Full System Simulation via Component-level Calibration

Karan Pathak, David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Joshua Alexander Harrison Klein

Full-System (FS) simulation is essential for performance evaluation of complete systems that execute complex applications on a complete software stack consisting of an operating system and user applications. Nevertheless, they require careful fine-tuning a ...

Association for Computing Machinery (ACM)2025

Design Optimization of 3D Multi-Processor System-on-Chip with Integrated Flow Cell Arrays

David Atienza Alonso, Marina Zapater Sancho, Artem Andreev

Integrated flow cell array (FCA) is an emerging technology, targeting the cooling and power delivery challenges of modern 2D/3D Multi-Processor Systems-on-Chip (MPSoCs). In FCA, electrolytic solutions are pumped through microchannels etched in the silicon ...

2018

Thermal and Voltage-Aware Performance Management of 3D MPSoCs with Flow Cell Arrays and Integrated SC Converters

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Halima Najibi

Flow cell arrays (FCAs) concurrently provide efficient on-chip liquid cooling and electrochemical power generation. This technology is especially promising for three-dimensional multi-processor systems-on-chip (3D MPSoCs) realized in deeply scaled technolo ...

2022