Publications de Benoît Walter Denkinger

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

David Atienza Alonso, Giovanni Ansaloni, José Angel Miranda Calero, Juan Pablo Sapriza Araujo, Rubén Rodríguez Álvarez, Benoît Walter Denkinger, Ruben Rodriguez

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 ...

2024

An Open-Hardware Coarse-Grained Reconfigurable Array for Edge Computing

David Atienza Alonso, Giovanni Ansaloni, José Angel Miranda Calero, Juan Pablo Sapriza Araujo, Rubén Rodríguez Álvarez, Benoît Walter Denkinger, Ruben Rodriguez

In this work, we propose an open-hardware low-power coarse-grained reconfigurable array connected to a lightweight microcontroller and enclosed in an application mapping framework. The latter provides complete support to configure kernels in the reconfigur ...

2023

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

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems

Benoît Walter Denkinger

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

EPFL2023

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

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

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

Bio-inspired adaptive impedance based controller for human-robot interaction and method

Mohamed Bouri, Amalric Louis Ortlieb, Benoît Walter Denkinger, Peter Lichard, Tommaso Tracchia

The method for controlling a single- or multi- powered robotic system, such as an exoskeleton, a prosthesis or a collaborative robot, that is physically interacting with a user, said system comprising at least one actuated joint; wherein the robot joint(s) ...

2020