Publications de Miguel Peon Quiros

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

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

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

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

David Atienza Alonso, Miguel Peon Quiros, Pasquale Davide Schiavone, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Dimitrios Samakovlis, 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, Simone Machetti, Pasquale Davide Schiavone, Thomas Christoph Müller, Rubén Rodríguez Álvarez, Benoît Walter Denkinger, Ruben Rodriguez, 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

Dynamic Scheduling for Event-Driven Embedded Industrial Applications

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

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

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

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

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

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

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

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

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

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