Overcoming quadratic hardware scaling for a fully connected digital oscillatory neural network.

ABSTRACT: Computing with coupled oscillators or oscillatory neural networks (ONNs) has recently attracted a lot of interest due to their potential for massive parallelism and energy-efficient computing. However, to date, ONNs have primarily been explored either analytically or through analog circuit implementations. This paper shifts the focus to the digital implementation of ONNs, examining various design architectures. We first report on an existing digital ONN design based on a recurrent architecture. The major challenge for scaling such recurrent architectures is the quadratic increase in coupling hardware with the network size. To overcome this challenge, we introduce a novel hybrid architecture that balances serialization and parallelism in the coupling elements that shows near-linear hardware

SUBMITTER: Haverkort BF 

PROVIDER: S-EPMC12851967 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature