324 citations · 535 across the 24 of their papers we have counts for
24 papers
A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems
José L. Risco-Martín, David Atienza, J. Manuel Colmenar +1
For the last thirty years, several Dynamic Memory Managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance…
Simulation of high-performance memory allocators
José L. Risco-Martín, J. Manuel Colmenar, David Atienza +1
For the last thirty years, a large variety of memory allocators have been proposed. Since performance, memory usage and energy consumption of each memory allocator differs, softwar…
CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds
Darong Huang, Luis Costero, Ali Pahlevan +2
Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside…
A Soft SIMD Based Energy Efficient Computing Microarchitecture
Pengbo Yu, Alexandre Levisse, Mohit Gupta +4
The ever-increasing size and computational complexity of today's machine-learning algorithms pose an increasing strain on the underlying hardware. In this light, novel and dedicate…
Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference
Marco Rios, Flavio Ponzina, Alexandre Levisse +2
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 arra…
Many-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems
Saleh Baghersalimi, Alireza Amirshahi, Farnaz Forooghifar +3
Integrating low-power wearable Internet of Things (IoT) systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables…
Event-based sampled ECG morphology reconstruction through self-similarity
Silvio Zanoli, Tomas Teijeiro, Giovanni Ansaloni +1
Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event…
HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration
William Andrew Simon, Una Pale, Tomas Teijeiro +1
HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. Howe…
VWR2A: A Very-Wide-Register Reconfigurable-Array Architecture for Low-Power Embedded Devices
Benoît Walter Denkinger, Miguel Peón-Quirós, Mario Konijnenburg +2
Edge-computing requires high-performance energy-efficient embedded systems. Fixed-function or custom accelerators, such as FFT or FIR filter engines, are very efficient at implemen…
Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection
Una Pale, Tomas Teijeiro, David Atienza
The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, an…
ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning
Joshua Klein, Irem Boybat, Yasir Qureshi +6
Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate…
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection
Una Pale, Tomas Teijeiro, David Atienza
Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal du…
Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise
Elisabetta De Giovanni, Tomas Teijeiro, Grégoire P. Millet +1
Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is gen…
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection
Una Pale, Tomas Teijeiro, David Atienza
Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. It requires new s…
An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors
Silvio Zanoli, Flavio Ponzina, Tomás Teijeiro +2
Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and sto…
ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm
Una Pale, Nathan Müller, Adriana Arza +1
This work presents ReBeatICG, a real-time, low-complexity beat-to-beat impedance cardiography (ICG) delineation algorithm that allows hemodynamic parameters monitoring. The propose…
Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health
Lara Orlandic, Adriana Arza Valdes, David Atienza
A person's passage of time perception (POTP) is strongly linked to their mental state and stress response, and can therefore provide an easily quantifiable means of continuous ment…
Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection
Una Pale, Tomas Teijeiro, David Atienza
Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epil…
ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals
Arman Iranfar, Adriana Arza, David Atienza
Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of wo…
The RECIPE Approach to Challenges in Deeply Heterogeneous High Performance Systems
Giovanni Agosta, William Fornaciari, David Atienza +8
RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, w…
Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals
Valentin Gabeff, Tomas Teijeiro, Marina Zapater +4
While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice a…
Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors
Sergi Abadal, Robert Guirado, Hamidreza Taghvaee +18
The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly c…
The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
Lara Orlandic, Tomas Teijeiro, David Atienza
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…
Markov Decision Process Based Energy-Efficient On-Line Scheduling for Slice-Parallel Video Decoders on Multicore Systems
Nicholas Mastronarde, Karim Kanoun, David Atienza +2
We consider the problem of energy-efficient on-line scheduling for slice-parallel video decoders on multicore systems. We assume that each of the processors are Dynamic Voltage Fre…