Oscillatory neural network learning for pattern recognition: an on-chip learning perspective and implementation
| Autor: | Abernot, Madeleine, Azemard, Nadine, Todri-Sanial, Aida |
|---|---|
| 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), Eindhoven University of Technology [Eindhoven] (TU/e), 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) |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]
FPGA implementation General Neuroscience pattern recognition Oscillatory neural network on-chip learning unsupervised learning [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [SPI.TRON]Engineering Sciences [physics]/Electronics |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, 2023, 17, ⟨10.3389/fnins.2023.1196796⟩ |
| ISSN: | 1662-4548 1662-453X |
| Popis: | International audience; In the human brain, learning is continuous, while currently in AI, learning algorithms are pre-trained, making the model non-evolutive and predetermined. However, even in AI models, environment and input data change over time. Thus, there is a need to study continual learning algorithms. In particular, there is a need to investigate how to implement such continual learning algorithms on-chip. In this work, we focus on Oscillatory Neural Networks (ONNs), a neuromorphic computing paradigm performing auto-associative memory tasks, like Hopfield Neural Networks (HNNs). We study the adaptability of the HNN unsupervised learning rules to on-chip learning with ONN. In addition, we propose a first solution to implement unsupervised on-chip learning using a digital ONN design. We show that the architecture enables efficient ONN on-chip learning with Hebbian and Storkey learning rules in hundreds of microseconds for networks with up to 35 fully-connected digital oscillators. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|