Coarse-Grained Reconfigurable Arrays

Research Line

Accelerators

CGRA

Keywords
accelerator, reconfigurable, heterogeneous, low-power, low-energy

Team

  Ansaloni Giovanni
  Atienza Alonso David
  Miranda Calero José Angel
  Orlandic Lara
  Rodriguez Álvarez Rubén
  Sapriza Araujo Juan Pablo
  Schiavone Pasquale Davide

Research Partners

IMEC IMEC

Sources of Funding

RESoRT
SNF ML-edge
Compusapien


CGRAs provide a good tradeoff in terms of throughput and energy efficiency. This project aims to explore hardware architecture in the context of CGRAs to improve the energy efficiency of low-power embedded systems.

News

  Open-hardware CGRA



Related Publications

An Open-Hardware Coarse-Grained Reconfigurable Array for Edge Computing
Rodríguez Álvarez, Rubén; Denkinger, Benoît Walter; Sapriza, Juan; Miranda Calero, José Angel; Ansaloni, Giovanni; Atienza Alonso, David
2023-04-25Conference PaperPublication funded by RESoRT (RESoRT: Reliable Epileptic Seizure Monitoring in Real Time)Publication funded by SNF ML-edge (ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization)
Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems
Denkinger, Benoît Walter; Peon Quiros, Miguel; Konijnenburg, Mario; Atienza Alonso, David; Catthoor, Francky
2023-03-17Transactions on ComputersPublication funded by RESoRT (RESoRT: Reliable Epileptic Seizure Monitoring in Real Time)Publication funded by SNF ML-edge (ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization)Publication funded by Compusapien (Next-gen computing systems inspired by the human brain)
VWR2A: A Very-Wide-Register Reconfigurable-Array Architecture for Low-Power Embedded Devices
Denkinger, Benoît Walter; Peon Quiros, Miguel; Konijnenburg, Mario; Atienza Alonso, David; Catthoor, Francky
2022Conference PaperPublication funded by Compusapien (Next-gen computing systems inspired by the human brain)Publication funded by RESoRT (RESoRT: Reliable Epileptic Seizure Monitoring in Real Time)Publication funded by SNF ML-edge (ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization)
Modular Design and Optimization of Biomedical Applications for Ultra-Low Power Heterogeneous Platforms
De Giovanni, Elisabetta; Montagna, Fabio; Denkinger, Benoît Walter; Machetti, Simone; Peon Quiros, Miguel; Benatti, Simone; Rossi, Davide; Benini, Luca; Atienza Alonso, David
2020[Proceedings of CODES+ISSS 2020]Publication funded by SNF ML-edge (ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization)Publication funded by Hasler MyPreHealth (Predicting Episodic Disorders with Health Companions)Publication funded by DeepHealth H2020 (Deep-Learning and HPC to Boost Biomedical Applications for Health)
i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing
Duch, Loris Gérard; Basu, Soumya Subhra; Peon Quiros, Miguel; Ansaloni, Giovanni; Pozzi, Laura; Atienza Alonso, David
2019IEEE Embedded Systems Letters (ESL)
Heterogeneous and Inexact: Maximizing Power Efficiency of Edge Computing Sensors for Health Monitoring Applications
Basu, Soumya Subhra; Duch, Loris Gérard; Peon Quiros, Miguel; Ansaloni, Giovanni; Pozzi, Laura; Atienza Alonso, David
20182018 Ieee International Symposium On Circuits And Systems (Iscas)
HEAL-WEAR: an Ultra-Low Power Heterogeneous System for Bio-Signal Analysis
L. Duch, S. Basu, R. Braojos, G. Ansaloni, L. Pozzi and D. Atienza
2017IEEE Transactions on Circuits and Systems-I (TCAS-I)
An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery
S. Basu, L. Duch, R. Braojos, G. Ansaloni, L. Pozzi and D. Atienza
2017CODES-ISSS, Seoul, South-Korea
A Multi-Core Reconfigurable Architecture for Ultra-Low Power Bio-Signal Analysis
L. Duch, S. Basu, R. Braojos, G. Ansaloni, L. Pozzi and D. Atienza
2016BioCAS, Shanghai, China