Neuromorphic Hebbian learning with magnetic tunnel junction synapses.

ABSTRACT: Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency. Conventional approaches store synaptic weights in non-volatile memory devices with analog resistance states, permitting in-memory computation of neural network operations while avoiding the costs of transferring synaptic weights from memory. However, the use of analog resistance states for storing weights in neuromorphic systems is impeded by stochastic writing, weights drifting over time through stochastic processes, and limited endurance that reduces the precision of synapse weights. Here we propose and experimentally demonstrate neuromorphic networks that provide high-accuracy inference thanks to the binary resistance states of ma

SUBMITTER: Zhou P 

PROVIDER: S-EPMC12322246 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature