David Atienza Alonso

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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 ...

Resistive switching memory technologies (RRAM) are seen by most of the scientific community as an enabler for Edge-level applications such as embedded deep Learning, AI or signal processing of audio and video signals. However, going beyond a "simple'' repl ...

New multimedia embedded applications are becoming increasingly dynamic. Thus, they cannot only rely on static data allocation, and must employ Dynamically-allocated Data Types (DDTs) to store their data and efficiently use the limited physical resources of ...

In this paper we present the top-down fabrication of gate-all-around (GAA) and body-tied @W-gate devices by a combination of etching and oxidation steps resulting in a local silicon-on-insulator structure. The GAA has advantages in terms of enhanced curren ...

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 ...

Objective: Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, ...

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 ...

Three dimensional stacked integrated circuits (3D ICs) are extremely attractive for overcoming the barriers in interconnect scaling, offering an opportunity to continue the CMOS performance trends for the next decade. However, from a thermal perspective, v ...

3D stacked circuits reduce communication delay in multicore system-on-chips (SoCs) and enable heterogeneous integration of cores, memories, sensors, and RF devices. However, vertical integration of layers exacerbates the reliability and thermal problems, a ...

This paper presents a novel method for classification of multilead electrocardiogram (ECG) signals. The feature extraction is based on the random projection (RP) concept for dimensionality reduction. Furthermore, the classification is performed by a neuro- ...

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 ...

Technology scaling has caused the feature sizes to shrink continuously, whereas interconnects, unlike transistors, have not followed the same trend. Designing 3D stack architectures is a recently proposed approach to overcome the power consumption and dela ...

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 ...

In the last decade, improvements on technology scaling have enabled the design of a novel generation of wearable bio-sensing monitors. These smart Wireless Body Sensor Nodes (WBSNs) are able to acquire and process biological signals, such as electrocardiog ...

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 ...

The need for efficient Convolutional Neural Network (CNNs) targeting embedded systems led to the popularization of Binary Neural Networks (BNNs), which significantly reduce execution time and memory requirements by representing the operands using only one ...

Meeting temperature constraints and reducing the hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. In this paper we aim at achieving an online smooth thermal control action that minimizes the performance l ...

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 ...

Overview Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. Th ...

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 ...

David Atienza Alonso

Chattez avec Graph Search

BLADE: A BitLine Accelerator for Devices on the Edge

Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems

Mixed Heuristic and Mathematical Programming Using Reference Points for Dynamic Data Types Optimization in Multimedia Embedded Systems

Prospects for logic-on-a-wire

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

Approximate Zero-Crossing: A new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

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

3D-ICE: Fast compact transient thermal modeling for 3D-ICs with inter-tier liquid cooling

Fuzzy Control for Enforcing Energy Efficiency in High-Performance 3D Systems

A Multi-Lead ECG Classification Based on Random Projection Features

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

Dynamic Thermal Management in 3D Multicore Architectures

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

A Synchronization-Based Hybrid-Memory Multi-Core Architecture for Energy-Efficient Biomedical Signal Processing

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

Accelerating Inference on Binary Neural Networks with Digital RRAM Processing

Multicore thermal management using approximate explicit model predictive control

Enhancing Two-Phase Cooling Efficiency through Thermal- Aware Workload Mapping for Power-Hungry Servers

The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

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