This is the list of publications that have been published in the Research pages of the ESL website:
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| ML-enabled IoT devices and embedded AI |
| UrbanTwin |
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| Epilepsy Monitoring |
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| Epilepsy Monitoring |
| UrbanTwin |
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| Inmodi knee brace |
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| Software-defined SIMD |
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| DIGIPREDICT: multi-biosignals processing for cardiovascular conditions |
| KID-PPG |
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| Efficient federated learning |
| Epilepsy Monitoring |
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| Coarse-Grained Reconfigurable Arrays |
| UrbanTwin |
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| Epilepsy Monitoring |
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| Software-defined SIMD |
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| Multi-objective management and optimization of servers and data centers |
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| Multi-objective management and optimization of servers and data centers |
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| Systolic Array Accelerators for Transformers |
| SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms | | Dan, Jonathan; Pale, Una; Amirshahi, Alireza; Cappelletti, William; Ingolfsson, Thorir Mar; Wang, Xiaying; Cossettini, Andrea; Bernini, Adriano; Benini, Luca; Beniczky, Sándor; Atienza, David; Ryvlin, Philippe | | 2024 | Conference Paper |  |
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| Epilepsy Monitoring |
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| ML-enabled IoT devices and embedded AI |
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| WiPLASH: In-package Wireless Links |
| 3D-ICE: 3D Interlayer Cooling Emulator |
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| Near Memory Computing |
| X-HEEP Poster - EcoCloud Event 2023 | | Machetti, Simone; Schiavone, Pasquale Davide; Müller, Christoph Thomas; Peon Quiros, Miguel; Atienza Alonso, David | | 2023-10-10 | Conference Paper |  |
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| X-HEEP: an open-hardware RISC-V system-on-chip |
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| Epilepsy Monitoring |
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| Sustainable computing for SKAO |
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| The COUGHVID crowdsourcing dataset |
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| Sustainable computing for SKAO |
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| gXR5: A gem5-based full-system RISC-V simulator |
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| Sparse sampling of bio-signals |
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| Health and wellness monitoring |
| X-HEEP: an open-hardware RISC-V system-on-chip |
| Coarse-Grained Reconfigurable Arrays |
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| Health and wellness monitoring |
| Coarse-Grained Reconfigurable Arrays |
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| ML-enabled IoT devices and embedded AI |
| Systolic Array Accelerators for Transformers |
| HEEPocrates, a tsmc65 implementation of X-HEEP |
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| Heterogeneous architectures for HPC applications |
| WiPLASH: In-package Wireless Links |
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| Epilepsy Monitoring |
| X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller | | Schiavone, Pasquale Davide; Machetti, Simone; Peon Quiros, Miguel; Miranda Calero, José Angel; Denkinger, Benoît Walter; Müller, Christoph Thomas; Rodríguez Álvarez, Rubén; Nasturzio, Saverio; Atienza Alonso, David | | 2023 | Conference Paper | |
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| X-HEEP: an open-hardware RISC-V system-on-chip |
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| Neural Network Quantization and Pruning |
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| DIGIPREDICT: multi-biosignals processing for cardiovascular conditions |
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| in-SRAM computing |
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| DIGIPREDICT: multi-biosignals processing for cardiovascular conditions |
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| Sparse sampling of bio-signals |
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| The COUGHVID crowdsourcing dataset |
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| Neural Network Quantization and Pruning |
| in-SRAM computing |
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| UrbanTwin |
| Epilepsy Monitoring |
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| Analog in Memory Cores |
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| Health and wellness monitoring |
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| Epilepsy Monitoring |
| Health and wellness monitoring |
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| Epilepsy Monitoring |
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| Sparse sampling of bio-signals |
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| ML-enabled IoT devices and embedded AI |
| Novel in-memory computing accelerators for Machine Learning (ML) on the edge |
| E2CNN: ensembles of CNN |
| HEEPocrates, a tsmc65 implementation of X-HEEP |
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| ML-enabled IoT devices and embedded AI |
| HEEPocrates, a tsmc65 implementation of X-HEEP |
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| HDTorch: HyperDimensional computing library |
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| Novel in-memory computing accelerators for Machine Learning (ML) on the edge |
| E2CNN: ensembles of CNN |
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| Sparse sampling of bio-signals |
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| Epilepsy Monitoring |
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| Multi-objective management and optimization of servers and data centers |
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| Epilepsy Monitoring |
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| Thermal-aware design of 2D/3D MPSoCs |
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| Health and wellness monitoring |
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| Thermal-aware design of 2D/3D MPSoCs |
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| Sparse sampling of bio-signals |
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| Sparse sampling of bio-signals |
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| Heterogeneous architectures for HPC applications |
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| ML-enabled IoT devices and embedded AI |
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| WiPLASH: In-package Wireless Links |
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| E2CNN: ensembles of CNN |
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| E2CNN: ensembles of CNN |
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| Coarse-Grained Reconfigurable Arrays |
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| in-SRAM computing |
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| The COUGHVID crowdsourcing dataset |
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| Thermal-aware design of 2D/3D MPSoCs |
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| HEEPocrates, a tsmc65 implementation of X-HEEP |
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| Multimodal and personalised methods for health and wellness monitoring |
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| Multimodal and personalised methods for health and wellness monitoring |
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| Epilepsy Monitoring |
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| E2CNN: ensembles of CNN |
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| Coughvid |
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| Multimodal and personalised methods for health and wellness monitoring |
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| Epilepsy Monitoring |
| ReBeatICG database | | Pale, Una; Meier, David; Müller, Olivier; Valdes, Adriana Arza; Alonso, David Atienza | | 2021-04-28 | Dataset | |
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| Sparse sampling of bio-signals |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
| Thermal-aware design of 2D/3D MPSoCs |
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| Health and wellness monitoring |
| Sparse sampling of bio-signals |
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| Federated machine learning over fog/edge/cloud architectures |
| Epilepsy Monitoring |
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| Thermal-aware design of 2D/3D MPSoCs |
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| Analog in Memory Cores |
| in-SRAM computing |
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| ML-enabled IoT devices and embedded AI |
| ML-enabled IoT devices and embedded AI |
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| gem5-X: gem5 made easy |
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| Power-aware acceleration of Deep Learning (DL) training and inference on High-Performance Computing (HPC) servers |
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| Multimodal and personalised methods for health and wellness monitoring |
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| Health and wellness monitoring |
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| Sparse sampling of bio-signals |
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| Epilepsy Monitoring |
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| E2CNN: ensembles of CNN |
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| Synthetic realistic noise-corrupted PPG database |
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| HDTorch: HyperDimensional computing library |
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| Power-aware acceleration of Deep Learning (DL) training and inference on High-Performance Computing (HPC) servers |
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| HEEPocrates, a tsmc65 implementation of X-HEEP |
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| Health and wellness monitoring |
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| Multimodal and personalised methods for health and wellness monitoring |
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| Epilepsy Monitoring |
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| Health and wellness monitoring |
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| Analog in Memory Cores |
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| Epilepsy Monitoring |
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| Epilepsy Monitoring |
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| Containergy |
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| Sparse sampling of bio-signals |
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| Multimodal and personalised methods for health and wellness monitoring |
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| Multimodal and personalised methods for health and wellness monitoring |
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| Analog in Memory Cores |
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| Sparse sampling of bio-signals |
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| Health and wellness monitoring |
| Enabling Optimal Power Generation of Flow Cell Arrays in 3D MPSoCs with On-Chip Switched Capacitor Converters | | Najibi, Halima; Hunter, Jorge; Levisse, Alexandre Sébastien Julien; Zapater Sancho, Marina; Vasic, Miroslav; Atienza Alonso, David | | 2020 | IEEE Computer Society Annual Symposium on VLSI, Limassol, Cyprus, July 6-8, 2020 |   |
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| Fuel Cell Arrays: liquid cooling and power delivery |
| Towards Deeply Scaled 3D MPSoCs with Integrated Flow Cell Array Technology | | Najibi, Halima; Levisse, Alexandre Sébastien Julien; Zapater Sancho, Marina; Aly, Mohamed Mostafa Sabry; Atienza Alonso, David | | 2020 | ACM Great Lakes Symposium on VLSI (GLSVLSI), Beijing, China, September, 7-9, 2020 | |
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| Fuel Cell Arrays: liquid cooling and power delivery |
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| Health and wellness monitoring |
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| Health and wellness monitoring |
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| Analog in Memory Cores |
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| Heterogeneous architectures for HPC applications |
|
| 3D-ICE: 3D Interlayer Cooling Emulator |
|
| Multimodal and personalised methods for health and wellness monitoring |
| Epilepsy Monitoring |
|
| Multimodal and personalised methods for health and wellness monitoring |
|
| Multi-objective management and optimization of servers and data centers |
|
| Coarse-Grained Reconfigurable Arrays |
|
| in-SRAM computing |
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| Analog in Memory Cores |
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| Federated machine learning over fog/edge/cloud architectures |
| Energy-efficient embedded machine learning |
| Resource management from the edge to the cloud, and efficient simulation of Internet-of-Things (IoT) scenarios for Artificial Intelligence (AI) applications |
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| Containergy |
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| Sparse sampling of bio-signals |
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| in-SRAM computing |
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| Analog in Memory Cores |
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| Federated machine learning over fog/edge/cloud architectures |
| Energy-efficient embedded machine learning |
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| Health and wellness monitoring |
|
| in-SRAM computing |
|
| in-SRAM computing |
|
| gem5-X: gem5 made easy |
|
| Analog in Memory Cores |
|
| Multimodal and personalised methods for health and wellness monitoring |
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| Energy-efficient embedded machine learning |
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| Fuel Cell Arrays: liquid cooling and power delivery |
|
| Multi-objective management and optimization of servers and data centers |
|
| Coarse-Grained Reconfigurable Arrays |
|
| Analog in Memory Cores |
|
| Multimodal and personalised methods for health and wellness monitoring |
|
| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
|
| Sparse sampling of bio-signals |
|
| Sparse sampling of bio-signals |
|
| Fuel Cell Arrays: liquid cooling and power delivery |
|
| Multi-objective management and optimization of servers and data centers |
|
| Multi-objective management and optimization of servers and data centers |
|
| Thermal-aware design of 2D/3D MPSoCs |
|
| Multimodal and personalised methods for health and wellness monitoring |
|
| Coarse-Grained Reconfigurable Arrays |
|
| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
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| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
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| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
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| Energy-efficient embedded machine learning |
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| Energy-efficient embedded machine learning |
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| Energy-efficient embedded machine learning |
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| Fuel Cell Arrays: liquid cooling and power delivery |
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| Coarse-Grained Reconfigurable Arrays |
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| Coarse-Grained Reconfigurable Arrays |
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| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
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| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
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| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
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| Energy-efficient embedded machine learning |
| Towards Near-Threshold Server Processors | | A. Pahlevan, J. Picorel Obando, A. Pourhabibi Zarandi, D. Rossi and M. Zapater Sancho et al. | | 2016 | Design, Automation and Test in Europe Conference (DATE), Dresden, Germany |  |
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| Heterogeneous architectures for HPC applications |
|
| Coarse-Grained Reconfigurable Arrays |
|
| Energy-aware design of multi-modal wearable sensors for personalized health and wellness monitoring |
| Sparse sampling of bio-signals |
| Energy-efficient embedded machine learning |
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| Fuel Cell Arrays: liquid cooling and power delivery |
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| Fuel Cell Arrays: liquid cooling and power delivery |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| STEAM |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| Energy-efficient embedded machine learning |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
| Towards Thermally-Aware Design of 3D MPSoCs with Inter-Tier Cooling | | Sabry, Mohamed; Sridhar, Arvind; Atienza Alonso, David; Temiz, Yuksel; Leblebici, Yusuf; Szczukiewicz, Sylwia; Borhani, Navid; Thome, John Richard; Brunschwiler, Thomas; Michel, Bruno | | 2011 | Proceedings of Design, Automation and Test in Europe (DATE) | |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
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| 3D-ICE: 3D Interlayer Cooling Emulator |
