Contributors: Laboratoire Nanotechnologies et Nanosystèmes Sherbrooke (LN2); Université de Sherbrooke = University of Sherbrooke Sherbrooke (UdeS)-École Centrale de Lyon (ECL); Université de Lyon-Université de Lyon-École Supérieure de Chimie Physique Électronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA); Institut Interdisciplinaire d'Innovation Technologique Sherbrooke (3IT); Université de Sherbrooke = University of Sherbrooke Sherbrooke (UdeS); Nanostructures, nanoComponents & Molecules - IEMN (NCM - IEMN); Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN); Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA); Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA); Université catholique de Lille (UCL)-Université catholique de Lille (UCL); STMicroelectronics Crolles (ST-CROLLES); SPINtronique et TEchnologie des Composants (SPINTEC); Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG); Direction de Recherche Fondamentale (CEA) (DRF (CEA)); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA); Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology Zürich (ETH Zürich); IBM Research Zurich; Institut des Matériaux, de Microélectronique et des Nanosciences de Provence (IM2NP); Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS); Centre de Nanosciences et de Nanotechnologies (C2N); Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS); We want to acknowledge engineers from 3IT, Sherbrooke, for TiO2 device fabrication. The authors acknowledge the staff of Binnig and Rohrer Nanotechnology Center (BRNC), Zurich, for HZO and CMO-HfO2 device fabrication. We acknowledge financial support from the EU: ERC-2017-COG project IONOS (# GA 773228) and CHIST-ERA UNICO project. This work was also supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) (No. 559730) and Fond de Recherche du Québec Nature et Technologies (FRQNT). FA thanks the MEIE for support through the chair in neuromorphic engineering.; ANR-19-CHR3-0006,UNICO,Unsupervised spiking neural networks with analog memristive devices for edge computing(2019); European Project: 773228,ERC-2017-COG,ERC-2017-COG,IONOS(2018)
نبذة مختصرة : International audience ; The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired learning mechanisms, allowing for real-time adaptation while using low-power. In-memory computing with nanoscale resistive memories may play a crucial role in enabling the execution of AI workloads on these edge devices. In this study, we introduce voltage-dependent synaptic plasticity (VDSP) as an efficient approach for unsupervised and local learning in memristive synapses based on Hebbian principles. This method enables online learning without requiring complex pulse-shaping circuits typically necessary for spike-timing-dependent plasticity (STDP). We show how VDSP can be advantageously adapted to three types of memristive devices (TiO2, HfO2-based metal-oxide filamentary synapses, and HfZrO4-based ferroelectric tunnel junctions (FTJ)) with disctinctive switching characteristics. System-level simulations of spiking neural networks incorporating these devices were conducted to validate unsupervised learning on MNIST-based pattern recognition tasks, achieving state-of-the-art performance. The results demonstrated over 83% accuracy across all devices using 200 neurons. Additionally, we assessed the impact of device variability, such as switching thresholds and ratios between high and low resistance state levels, and proposed mitigation strategies to enhance robustness.
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