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David Atienza Alonso

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Related publications (385)

An Event-Based System for Low-Power ECG QRS Complex Detection

David Atienza Alonso, Silvio Zanoli, Tomas Teijeiro Campo, Fabio Montagna

One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmis ...

IEEE2020

Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions

David Atienza Alonso, Fabio Isidoro Tiberio Dell'Agnola, Adriana Arza Valdes, Niloofar Momeni

High levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observabl ...

IEEE2019

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

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

David Atienza Alonso, Amir Aminifar, Farnaz Forooghifar

The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applica ...

2019

REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm *

Jean-Marc Vesin, David Atienza Alonso, Sasan Yazdani, Adriana Arza Valdes, Elisabetta De Giovanni, Lara Orlandic

Wearable devices are an unobtrusive, cost-effective means of continuous ambulatory monitoring of chronic cardiovascular diseases. However, on these resource-constrained systems, electrocardiogram (ECG) processing algorithms must consume minimal power and m ...

2019

Teleoperation with a wearable sensor system

Dario Floreano, David Atienza Alonso, Jenifer Cléa Miehlbradt, Alexandre Thierry Cherpillod, Fabio Isidoro Tiberio Dell'Agnola, Silvestro Micera, Martina Coscia, Stefano Mintchev, Fiorenzo Artoni

A method for remotely controlling an operated unmanned object, comprises defining of a set of control movements of an operator; selecting of minimal necessary signals to reliably acquire the operator's control movements; defining of a mapping of the contro ...

2019

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

Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices

David Atienza Alonso, Amir Aminifar, Fabio Isidoro Tiberio Dell'Agnola, Adriana Arza Valdes, Victoriano Montesinos Canovas

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this pap ...

IEEE2019

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing

David Atienza Alonso, Soumya Subhra Basu, Giovanni Ansaloni, Loris Gérard Duch, Miguel Peon Quiros

Smart edge sensors for bio-signal monitoring must support complex signal processing routines within an extremely small energy envelope. Coarse-Grained Reconfigurable Arrays (CGRAs) are good candidates for tackling these conflicting objectives because, than ...

2019

Functionality Enhanced Memories for Edge-AI Embedded Systems

David Atienza Alonso, Pierre-Emmanuel Julien Marc Gaillardon, William Andrew Simon, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

With the surge in complexity of edge workloads, it appeared in the scientific community that such workloads cannot be anymore overflown to the cloud due to the huge edge device to server communication energy cost and the high energy consumption induced in ...

IEEE2019

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

Tailoring SVM Inference for Resource-Efficient ECG-Based Epilepsy Monitors

David Atienza Alonso, Amir Aminifar, Leila Cammoun, Giovanni Ansaloni

Event detection and classification algorithms are resilient towards aggressive resource-aware optimisations. In this paper, we leverage this characteristic in the context of smart health monitoring systems. In more detail, we study the attainable benefits ...

IEEE2019

A Self-Learning Methodology for Epileptic Seizure Detection with Minimally Supervised Edge Labeling

David Atienza Alonso, Amir Aminifar, Damián Pascual Ortiz

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortali ...

2019

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

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

David Atienza Alonso, Xifan Tang, Pierre-Emmanuel Julien Marc Gaillardon, Edouard Giacomin, Marina Zapater Sancho, Yasir Mahmood Qureshi, João Miguel Morgado Pereira Vieira

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. ...

2019

Core Cosmology Library: Precision Cosmological Predictions for LSST

David Atienza Alonso, Jonathan Andrew Blazek

The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests. Predictions are provided for many cosmological quantities, incl ...

IOP PUBLISHING LTD2019

LSST: From Science Drivers to Reference Design and Anticipated Data Products

David Atienza Alonso, Guillaume Blanc, Shahram Khazaei, Christopher Montgomery, Darko Jevremovic, Abhijit Saha, Anja Annemone Becker, Michelle Miller, Jonathan Andrew Blazek, Darong Huang, Shikhar Tuli, Wei Cui

We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, ex ...

IOP PUBLISHING LTD2019

Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

David Atienza Alonso, Amir Aminifar, Grégoire Casimir Joseph Surrel, Tomas Teijeiro Campo

In the context of wearable medical systems, resources are scarce while performance requirements are high. Traditional sampling strategies create large amounts of data, which hinders the device's battery lifetime. However, energy savings are possible when r ...

2019

A wearable system for real-time detection of epileptic seizures

David Atienza Alonso, Amir Aminifar, Dionisije Sopic, Renato Zanetti

A wearable system for epileptic seizure detection, comprising an eyeglasses frame, with a left arm and a right arm configured to rest over the ears of an intended person wearing the eyeglasses, a first pair of electrodes located in the left arm, and a seco ...

2019

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