Publications de Miguel Peon Quiros

Publications associées (19)

E2CNN: Ensembles of Convolutional Neural Networks to Improve Robustness Against Memory Errors in Edge-Computing Devices

David Atienza Alonso, Andreas Peter Burg, Miguel Peon Quiros, Flavio Ponzina

To reduce energy consumption, it is possible to operate embedded systems at sub-nominal conditions (e.g., reduced voltage, limited eDRAM refresh rate) that can introduce bit errors in their memories. These errors can affect the stored values of CNN weights ...

2021

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

David Atienza Alonso, Amir Aminifar, Miguel Peon Quiros, Adriana Arza Valdes, Elisabetta De Giovanni

In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel compu ...

2021

A hardware/software co-design vision for deep learning at the edge

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Flavio Ponzina, Marco Antonio Rios, Benoît Walter Denkinger, Simone Machetti

The growing popularity of edgeAI requires novel solutions to support the deployment of compute-intense algorithms in embedded devices. In this article, we advocate for a holistic approach, where application-level transformations are jointly conceived with ...

2022

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

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Marco Antonio Rios

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 ...

IEEE2020

Dynamic Scheduling for Event-Driven Embedded Industrial Applications

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

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

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

David Atienza Alonso, Miguel Peon Quiros, Benoît Walter Denkinger

Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

2023

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

2022

Analysis of Functional Errors Produced by Long-Term Workload-Dependent BTI Degradation in Ultra-Low Power Processors

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Ruben Braojos Lopez, Loris Gérard Duch

Aging effects in digital circuits change the switching characteristics of their transistors, resulting in timing violations that can lead to functional errors at the system level. In particular, BTI is a degradation effect that changes the threshold voltag ...

2020

Special Session: Challenges and Opportunities for Sustainable Multi-Scale Computing Systems

Xavier Eric Ouvrard, David Atienza Alonso, Miguel Peon Quiros

Multi-Scale computing systems aim at bringing the computing as close as possible to the data sources, to optimize both computation and networking. These systems are composed of at least three computing layers: the terminal layer, the edge layer, and the cl ...

ACM2023

Impact of Memory Voltage Scaling on Accuracy and Resilience of Deep Learning Based Edge Devices

David Atienza Alonso, Andreas Peter Burg, Miguel Peon Quiros, Martino Ruggiero, Andrea Bonetti, Flavio Ponzina, Benoît Walter Denkinger, Soumya Subhra Basu, Szabolcs Balási

Energy consumption is a significant obstacle to integrate deep learning into edge devices. Two common techniques to curve it are quantization, which reduces the size of the memories (static energy) and the number of accesses (dynamic energy), and voltage s ...

2020

VWR2A: A Very-Wide-Register Reconfigurable-Array Architecture for Low-Power Embedded Devices

David Atienza Alonso, Miguel Peon Quiros, Benoît Walter Denkinger

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 implementing a particular functionality for a given set of constraints. However, the ...

ASSOC COMPUTING MACHINERY2022

Cross-layer Exploration of 2.5D Energy-Efficient Heterogeneous Chiplets Integration: From System Simulation to Open Hardware

Anna Burdina, David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Pasquale Davide Schiavone

In the past decade, computing systems have significantly increased in complexity and power consumption. Nowadays, heterogeneous multi-processor systems-on-chip (MPSoCs) integrate many computing cores. Heterogeneous MPSoCs often comprise general-purpose pro ...

Association for Computing Machinery, Inc2024

Heterogeneous and Inexact: Maximizing Power Efficiency of Edge Computing Sensors for Health Monitoring Applications

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Soumya Subhra Basu, Loris Gérard Duch

In the Internet-of-Things (IoT) era, there is an increasing trend to enable intelligent behavior in edge computing sensors. Thus, a new generation of smart wearable devices for health monitoring is being developed, able to perform complex Digital Signal Pr ...

IEEE2018

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Soumya Subhra Basu, Loris Gérard Duch

Smart edge sensors for bio-signal monitoring must support complex signal processing routines within an extremely small energy envelope. Coarse-Grained Reconfigurable Arrays (CGRAs) are good candidates for tackling these conflicting objectives because, than ...

2019

Modular Design and Optimization of Biomedical Applications for Ultra-Low Power Heterogeneous Platforms

David Atienza Alonso, Luca Benini, Miguel Peon Quiros, Benoît Walter Denkinger, Elisabetta De Giovanni, Simone Machetti

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable senso ...

2020

Heterogeneous Memory Organizations in Embedded Systems. Placement of Dynamic Data Objects

Miguel Peon Quiros

This book defines and explores the problem of placing the instances of dynamic data types on the components of the heterogeneous memory organization of an embedded system, with the final goal of reducing energy consumption and improving performance. It is ...

Springer International Publishing2020

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables

Dimitrios Samakovlis, David Atienza Alonso, Miguel Peon Quiros, Pasquale Davide Schiavone, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Stefano Albini

The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despit ...

Institute of Electrical and Electronics Engineers (IEEE)2024

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

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

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

Using Algorithmic Transformations and Sensitivity Analysis to Unleash Approximations in CNNs at the Edge

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Previous studies have demonstrated that, up to a certain degree, Convolutional Neural Networks (CNNs) can tolerate arithmetic approximations. Nonetheless, perturbations must be applied judiciously, to constrain their impact on accuracy. This is a challengi ...

MDPI2022