Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Frontiers in Neuroscience Année : 2024

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN

Résumé

In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior. Most proposals in the literature are based on hardware-independent algorithms that require the network to store the spiking history to be able to update the weights accordingly. In this work, we developed a new learning rule, the Bi-Sigmoid STDP (B2STDP), which originates from the physical properties of MTJs. This rule enables immediate synaptic plasticity based on neuronal activity, leveraging in-memory computing. Finally, the integration of this learning approach within an SNN framework leads to a 91.71% accuracy in unsupervised image classification, demonstrating the potential of MTJ-based synapses for effective online learning in hardware-implemented SNNs.

Fichier principal
Vignette du fichier
fnins-18-1387339.pdf (3.12 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04577320 , version 1 (16-05-2024)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Salah Daddinounou, Ioana Vatajelu. Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN. Frontiers in Neuroscience, 2024, 18, pp.1387339. ⟨10.3389/fnins.2024.1387339⟩. ⟨hal-04577320⟩

Collections

UGA CNRS TIMA
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More