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David Atienza Alonso

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Publications associées (385)

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, José Angel Miranda Calero, Jonathan Dan

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

2024

Cross-layer Exploration of 2.5D Energy-Efficient Heterogeneous Chiplets Integration: From System Simulation to Open Hardware

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Anna Burdina, Pasquale Davide Schiavone

In the past decade, computing systems have significantly increased in complexity and power consumption. Nowadays, heterogeneous multi-processor systems-on-chip (MPSoCs) integrate many computing cores. Heterogeneous MPSoCs often comprise general-purpose pro ...

Association for Computing Machinery, Inc2024

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables

David Atienza Alonso, Miguel Peon Quiros, Dimitrios Samakovlis, Stefano Albini, Pasquale Davide Schiavone, Denisa-Andreea Constantinescu, Rubén Rodríguez Álvarez

The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despit ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Which Coupled is Best Coupled? An Exploration of AIMC Tile Interfaces and Load Balancing for CNNs

David Atienza Alonso, Giovanni Ansaloni, Irem Boybat Kara, Joshua Alexander Harrison Klein

Due to stringent energy and performance constraints, edge AI computing often employs heterogeneous systems that utilize both general-purpose CPUs and accelerators. Analog in-memory computing (AIMC) is a well-known AI inference solution that overcomes compu ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

2024

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

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

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

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

David Atienza Alonso, Lara Orlandic, Tomas Teijeiro Campo, Jérôme Paul Rémy Thevenot, Jonathan Dan

Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Learning (ML) algorithms are promising for quantifying daily coughs, providing c ...

IEEE2024

PlugVFL: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties

David Atienza Alonso, Saleh Baghersalimi, Alireza Amirshahi

In federated learning systems, the unexpected quitting of participants is inevitable. Such quittings generally do not incur serious consequences in horizontal federated learning (HFL), but they do damage to vertical federated learning (VFL), which has been ...

Institute of Electrical and Electronics Engineers Inc.2024

An Evaluation Framework for Dynamic Thermal Management Strategies in 3D MultiProcessor System-on-Chip Co-Design

David Atienza Alonso, Luis Maria Costero Valero, Darong Huang

Dynamic thermal management (DTM) has been widely adopted to improve the energy efficiency, reliability, and performance of modern MultiProcessor SoCs (MPSoCs) on runtime. However, the evolving industry trends and heterogeneous architecture designs have int ...

2024

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms

David Atienza Alonso, William Cappelletti, Una Pale, Alireza Amirshahi, Jonathan Dan

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms in ...

2024

An Energy Efficient Soft SIMD Microarchitecture and Its Application on Quantized CNNs

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Pengbo Yu

The ever-increasing computational complexity and energy consumption of today's applications, such as Machine Learning (ML) algorithms, not only strain the capabilities of the underlying hardware but also significantly restrict their wide deployment at the ...

2024

SAT-based Exact Modulo Scheduling Mapping for Resource-Constrained CGRAs

Ruben Rodriguez, David Atienza Alonso, Benoît Walter Denkinger, Giovanni Ansaloni, José Angel Miranda Calero, Juan Pablo Sapriza Araujo, Rubén Rodríguez Álvarez

Coarse-Grain Reconfigurable Arrays (CGRAs) represent emerging low-power architectures designed to accelerate Compute-Intensive Loops (CILs). The effectiveness of CGRAs in providing acceleration relies on the quality of mapping: how efficiently the CIL is c ...

2024

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, Flavio Ponzina, Amirhossein Shahbazinia, José Angel Miranda Calero, Jonathan Dan

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

IEEE2024

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

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Qunyou Liu

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 ...

2024

Performance evaluation of acceleration of convolutional layers on OpenEdgeCGRA

David Atienza Alonso, Pasquale Davide Schiavone, Juan Pablo Sapriza Araujo

Recently, efficiently deploying deep learning solutions on the edge has received increasing attention. New platforms are emerging to support the increasing demand for flexibility and high performance. In this work, we explore the efficient mapping of convo ...

Association for Computing Machinery, Inc2024

TEE4EHR: Transformer event encoder for better representation learning in electronic health records

Anisoara Ionescu, David Atienza Alonso

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisi ...

2024

Combining general and personal models for epilepsy detection with hyperdimensional computing

David Atienza Alonso, Una Pale, Tomas Teijeiro Campo

Epilepsy is a highly prevalent chronic neurological disorder with great negative impact on patients' daily lives. Despite this there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday li ...

Elsevier2024

FVLLMONTI: The 3D Neural Network Compute Cube (N2C2) Concept for Efficient Transformer Architectures Towards Speech-to-Speech Translation

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

This multi-partner-project contribution introduces the midway results of the Horizon 2020 FVLLMONTI project. In this project we develop a new and ultra-efficient class of ANN accelerators, the neural network compute cube (N2C2), which is specifically desig ...

Institute of Electrical and Electronics Engineers Inc.2024

ACE: Automated optimization towards iterative Classification in Edge health monitors

David Atienza Alonso, Giovanni Ansaloni, Lara Orlandic, Simone Machetti

Wearable devices for health monitoring are essential for tracking individuals' health status and facilitating early detection of diseases. However, processing biomedical signals online for real-time monitoring is challenging due to limited computational re ...

Institute of Electrical and Electronics Engineers (IEEE)2024

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

David Atienza Alonso, Lara Orlandic, Tomas Teijeiro Campo

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...

2023

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