Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks.

ABSTRACT: Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system ca

SUBMITTER: Stenning KD 

PROVIDER: S-EPMC11350220 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature