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, Antoine Jérôme Spahr, Jonathan Dan

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, Giovanni Ansaloni, Irem Boybat Kara, Marina Zapater Sancho, Abu Sebastian, Flavio Ponzina, Joshua Alexander Harrison Klein

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

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

SideDRAM: Integrating SoftSIMD Datapaths near DRAM Banks for Energy-Efficient Variable Precision Computation

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Rafael Medina Morillas, Pengbo Yu

By interfacing computing logic directly to the DRAM banks, bank-level Compute-near-Memory (CnM) architectures promise to mitigate the bottleneck at the memory interconnect. While this computation paradigm heavily reduces the energy requirements for data mo ...

ACM2025

Multi-Partner Project: Sustainable Textile Electronics (STELEC)

David Atienza Alonso, José Angel Miranda Calero, Jonathan Dan

E-textiles are rapidly emerging as an important area of electronic circuit applications. It also facilitates many socially important applications such as personalized health, elderly care, and smart agriculture. However, the environmental impact and sustai ...

IEEE2025

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

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

A Reconfigurable High Dynamic Range Delta-Sigma Front End with Event-Based Decimation for Bandwidth-EfficientImplantable 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 ...

2025

A 20-Year Retrospective on Power and Thermal Modeling and Management

David Atienza Alonso, Darong Huang, Kai Zhu

As processor performance advances, increasing power densities and complex thermal behaviors threaten both energy efficiency and system reliability. This survey covers more than two decades of research on power and thermal modeling and management in modern ...

2025

An MLIR-based Compilation Framework for CGRA Application Deployment

David Atienza Alonso, Giovanni Ansaloni, Lara Orlandic, Juan Pablo Sapriza Araujo, Rubén Rodríguez Álvarez

Coarse-Grained Reconfigurable Arrays (CGRAs) are a programmable architectural solution that efficiently supports the execution of computation-intensive application functions (kernels). Compilers for CGRAs typically focus on optimizing the mapping of single ...

2025

A flexible framework for early power and timing comparison of time-multiplexed CGRA kernel executions

David Atienza Alonso, Giovanni Ansaloni, Juan Pablo Sapriza Araujo

At the intersection between traditional CPU architectures and more specialized options such as FPGAs or ASICs lies the family of reconfigurable hardware architectures, termed Coarse-Grained Reconfigurable Arrays (CGRAs). CGRAs are composed of a 2-dimension ...

ACM2025

FADE: Forecasting for anomaly detection on ECG

David Atienza Alonso, Denisa-Andreea Constantinescu

Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

Elsevier BV2025

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Saleh Baghersalimi, Farnaz Forooghifar, Tomas Teijeiro Campo, Alireza Amirshahi

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

2024

Is the powersave governor really saving power?

David Atienza Alonso, Luis Maria Costero Valero, Darong Huang

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

2024