EU H2020 NEURONN: 2D Oscillatory Neural Networks For Energy Efficient Neuromorphic Computing
| Autor: | Carapezzi, Stefania, Boschetto, Gabriele, Delacour, Corentin, Abernot, Madeleine, Gil, Thierry, Todri-Sanial, Aida |
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| Přispěvatelé: | Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871501., European Project: 871501,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),H2020-ICT-2019-2,NeurONN(2020), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Molybdenum disulfide (MoS2)
Neuromorphic computing pattern recognition Beyond-CMOS device [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET] Memristor [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics vanadium dioxide (VO2) [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation FPGA Oscillatory Neural Network (ONN) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [SPI.MAT]Engineering Sciences [physics]/Materials |
| Zdroj: | 15ème Colloque National du GDR SoC² 15ème Colloque National du GDR SoC², Jun 2021, Rennes, France. 2021 15ème Colloque National du GDR SoC², Jun 2021, Rennes, France., 2021 |
| Popis: | National audience; In this paper, we showcase a leading-edge implementation of oscillatory neural networks (ONNs) using beyond Complementary-Metal-Oxide-Semiconductor devices based on vanadium dioxide to mimick neurons, and 2D molybdenum disulfide memristors to emulate synapses. We explore the ONN technology through simulations from materials to devices up to circuits. We show that ONNs naturally behave like associative memories and can be used for pattern recognition, a task to be exploited in edge devices. Finally, we develop a reconfigurable digital ONN-on-FPGA to assess ONN functionality in real world applications. |
| Databáze: | OpenAIRE |
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