Publications de David Atienza Alonso

TimEHR: Image-based Time Series Generation for Electronic Health Records

Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper, we propose a novel generative adversarial network (GAN) model, TimEHR, to g ...

Institute of Electrical and Electronics Engineers (IEEE)2025

A Reconfigurable High-Dynamic Range ∆Σ Front-End with Event-Based Decimation for Bandwidth-Efficient Implantable Neural Interfaces

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

As the demand for high channel counts and high-resolution recordings of neural activity continues to grow, the increased power and data rate generated impose hard constraints on the telemetry capabilities of wireless implantable neural interfaces. To addre ...

IEEE2025

HEEPstor: an Open-Hardware Co-design Framework for Quantized Machine Learning at the Edge

David Atienza Alonso, Giovanni Ansaloni, Rafael Medina Morillas

Edge-AI applications necessitate the joint application of hardware acceleration and software optimization to meet energy and area constraints. However, the co-design of these systems is hindered by the lack of integration options for novel hardware prototy ...

ACM2025

Keep All in Memory with Maxwell: a Near-SRAM Computing Architecture for Edge AI Applications

David Atienza Alonso, Giovanni Ansaloni, Grégoire Axel Eggermann

Recent advances in machine learning have dramatically increased model size and computational requirements, increasingly straining computing system capabilities. This tension is particularly acute for resource-constrained edge scenarios, for which careful h ...

IEEE2025

Autonomous exploration of the PBF-LB parameter space: An uncertainty-driven algorithm for automated processing map generation

David Atienza Alonso, Giulio Masinelli

Powder bed fusion with laser beam (PBF-LB) is a promising additive manufacturing technique that enables the production of complex geometries with fine resolution and material efficiency, offering significant design freedom and material versatility. However ...

2025

Deep learning–based detection of generalized convulsive seizures using a wrist‐worn accelerometer

David Atienza Alonso, Jonathan Dan, Antoine Jérôme Spahr

Objective To develop and validate a wrist‐worn accelerometer‐based, deep‐learning tunable algorithm for the automated detection of generalized or bilateral convulsive seizures (CSs) to be integrated with off‐the‐shelf smartwatches. Methods We conducted a ...

Wiley2025

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

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

The Development of Embedded Computing as a Discipline [special Section on 2025 Ieee Kirchhoff Award]

David Atienza Alonso

To mark the occasion of festschrift of Prof. Giovanni De Micheli, this paper takes a retrospective look at research though the lines pursued by Prof. De Micheli that led to the emergence of Embedded Systems as an enduring discipline. In telling this narrat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge

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

Continuous cough monitors can greatly benefit doctors in home monitoring and treatment of respiratory diseases. Although many works propose algorithms to automate this task, they suffer of poor data privacy and short-term monitoring. Edge-AI is a promising ...

2025

Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems

David Atienza Alonso, Giovanni Ansaloni, Rafael Medina Morillas

Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic array ...

Association for Computing Machinery2025