Miguel Peon Quiros

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Publications associées (20)

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

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

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

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

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

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

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

David Atienza Alonso, Andreas Peter Burg, Miguel Peon Quiros, Andrea Bonetti, Martino Ruggiero, Benoît Walter Denkinger, Flavio Ponzina, 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

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

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

Results from Running an Experiment as a Service Platform for Mobile Broadband Networks in Europe

Miguel Peon Quiros, Cise Midoglu

2019

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

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

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