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Marina Zapater Sancho

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Related publications (59)

Exact Neural Networks from Inexact Multipliers via Fibonacci Weight Encoding

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

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

Memory chip or memory array for wide-voltage range in-memory computing using bitline technology

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

A random access memory having a memory array having a plurality of local memory groups, each local memory group including a plurality of bitcells arranged in a bitcell column, a pair of local bitlines operatively connected to the plurality of bitcells, a p ...

2021

Interpreting deep learning models for epileptic seizure detection on EEG signals

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

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

Dynamic Thermal Management with Proactive Fan Speed Control Through Reinforcement Learning

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Federico Terraneo, Gabor Andras Csordas

Dynamic Thermal Management (DTM) has become a major challenge since it directly affects Multiprocessors Systems-on-chip (MPSoCs) performance, power consumption, and reliability. In this work, we propose a transient fan model, enabling adaptive fan speed co ...

IEEE2020

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

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Wellington Silva De Souza

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

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

Enabling Optimal Power Generation of Flow Cell Arrays in 3D MPSoCs with On-Chip Switched Capacitor Converters

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

Flow cell arrays (FCAs) provide efficient on-chip liquid cooling and electrochemical power generation capabilities in three-dimensional multi-processor systems-on-chip (3DMPSoCs). When connected to power delivery networks (PDNs) of chips, the current flowi ...

IEEE2020

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

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

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

Blade: An in-Cache Computing Architecture for Edge Devices

David Atienza Alonso, William Andrew Simon, Marina Zapater Sancho, Yasir Mahmood Qureshi, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

Area and power constrained edge devices are increasingly utilized to perform compute intensive workloads, necessitating increasingly area and power efficient accelerators. In this context, in-SRAM computing performs hundreds of parallel operations on spati ...

2020

Predictive Reliability and Fault Management in Exascale Systems

David Atienza Alonso, Marina Zapater Sancho

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 ...

2020

The RECIPE approach to challenges in deeply heterogeneous high performance systems

David Atienza Alonso, Marina Zapater Sancho

RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring new High-Performance Computing (HPC) ...

ELSEVIER2020

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

David Atienza Alonso, Marina Zapater Sancho, Yasir Mahmood Qureshi, José Manuel Herruzo Ruiz

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 ...

2020

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

David Atienza Alonso, Mohamed Mostafa Sabry Aly, Halima Najibi, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse

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, ...

2020

Accelerating Inference on Binary Neural Networks with Digital RRAM Processing

David Atienza Alonso, Marina Zapater Sancho, Yasir Mahmood Qureshi

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 ...

Springer Nature2020

BLADE: A BitLine Accelerator for Devices on the Edge

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

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 ...

2019

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

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

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

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

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Arman Iranfar

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 ...

IEEE and ACM Press2019

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

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

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 ...

IEEE2019

Definition Of A Transparent Constraint-Based Modeling And Simulation Layer For The Management Of Complex Systems

Marina Zapater Sancho, Kevin Henares Vilaboa, José Luis Risco Martin

Modeling and Simulation (M&S) is one of the most multifaceted topics present today in both industry and academia. However, we are involved in a new M&S paradigm. Systems are becoming more complex and new simulation needs arise and have to be studied. As a ...

IEEE2019

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

David Atienza Alonso, William Andrew Simon, Marina Zapater Sancho, 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

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