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David Atienza Alonso

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Related publications (385)

Event-based sampled ECG morphology reconstruction through self-similarity

David Atienza Alonso, Giovanni Ansaloni, Silvio Zanoli, Tomas Teijeiro Campo

Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with ...

2023

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

2023

A wearable to assess knee joint integrity using non-contact acoustic sensors

Kamiar Aminian, David Atienza Alonso, Jérôme Paul Rémy Thevenot

A smart wearable (SW) for collecting data on an integrity of a joint of a wearer, the wearable including a first frame (1) configured to be attached in proximity of the joint of the wearer, the frame (1) including an elastic sensor frame (3) having an open ...

2023

An Open-Hardware Coarse-Grained Reconfigurable Array for Edge Computing

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

In this work, we propose an open-hardware low-power coarse-grained reconfigurable array connected to a lightweight microcontroller and enclosed in an application mapping framework. The latter provides complete support to configure kernels in the reconfigur ...

2023

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors

David Atienza Alonso, Silvio Zanoli, Alexandre Sébastien Julien Levisse, Tomas Teijeiro Campo, Flavio Ponzina

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon th ...

2023

TiC-SAT: Tightly-coupled Systolic Accelerator for Transformers

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi, Joshua Alexander Harrison Klein

Transformer models have achieved impressive results in various AI scenarios, ranging from vision to natural language processing. However, their computational complexity and their vast number of parameters hinder their implementations on resource-constraine ...

2023

System-Level Exploration of In-Package Wireless Communication for Multi-Chiplet Platforms

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Joshua Alexander Harrison Klein, Rafael Medina Morillas

Multi-Chiplet architectures are being increasingly adopted to support the design of very large systems in a single package, facilitating the integration of heterogeneous components and improving manufacturing yield. However, chiplet-based solutions have to ...

2023

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

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

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase rema ...

2023

Object-oriented modelling of advanced computer cooling solutions

David Atienza Alonso, Federico Terraneo

Modern computing systems are so energy- intensive to make efficient cooling vital for their operation. This is giving rise to a variety of innovative cooling solutions based on a mix of traditional and new techniques. The design and engineering of these so ...

Elsevier2023

Overflow-free compute memories for edge AI acceleration

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

2023

Overflow-free Compute Memories for Edge AI Acceleration

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

Assoc Computing Machinery2023

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller

Ruben Rodriguez, David Atienza Alonso, Benoît Walter Denkinger, Thomas Christoph Müller, Miguel Peon Quiros, Saverio Nasturzio, Simone Machetti, Pasquale Davide Schiavone, José Angel Miranda Calero, Rubén Rodríguez Álvarez

In this work, we present eXtendible Heterogeneous Energy-Efficient Platform (X-HEEP), a configurable and extendible single-core RISC-V-based ultra-low-power microcontroller. X-HEEP can be used standalone as a low-cost microcontroller, or it can be integrat ...

New York2023

Cross Layer Design for the Predictive Assessment of Technology-Enabled Architectures

David Atienza Alonso, Giovanni Ansaloni

There is great interest in "end-to-end" analysis that captures how innovation at the materials, device, and/or architectural levels will impact figures of merit at the application-level. However, there are numerous combinations of devices and architectures ...

IEEE2023

A Multimodal Dataset for Automatic Edge-AI Cough Detection

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

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while runnin ...

New York2023

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Saleh Baghersalimi, Tomas Teijeiro Campo

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains ...

2023

REMOTE: Re-thinking Task Mapping on Wireless 2.5D Systems-on-Package for Hotspot Removal

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Darong Huang, Rafael Medina Morillas

2.5D Systems-on-Package (SoPs) are composed by several chiplets placed on an interposer. They are becoming increasingly popular as they enable easy integration of electronic components in the same package and high fabrication yields. Nevertheless, they int ...

2023

Dynamic Scheduling for Event-Driven Embedded Industrial Applications

David Atienza Alonso, Szabolcs Balási, Miguel Peon Quiros, Hossein Taji, José Angel Miranda Calero

This paper addresses the optimization of embedded platforms to meet the computing and real-time requirements of cyber-physical systems and IoT applications, including embedded intelligence. In this context, schedulers are vital in enhancing processor utili ...

New York2023

A Multimodal Dataset for Automatic Edge-AI Cough Detection

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

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform ...

Zenodo2023

Importance of methodological choices in data manipulation for validating epileptic seizure detection models

David Atienza Alonso, Una Pale, Tomas Teijeiro Campo

Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life. Despite advances in machine learning and IoT, small, non-stigmatizing wearable devices for continuous monito ...

New York2023

Point-process-based Representation Learning for Electronic Health Records

Anisoara Ionescu, David Atienza Alonso

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

IEEE2023

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