Paper Title: Digital Implementation of Oscillatory Neural Network for Image Recognition Applications
Computing paradigm based on von Neuman architectures cannot keep up with the ever-increasing data growth (also called “data deluge gap”). This has resulted in investigating novel computing paradigms and design approaches at all levels from materials to system-level implementations and applications. An alternative computing approach based on artificial neural networks uses oscillators to compute or Oscillatory Neural Networks (ONNs). ONNs can perform computations efficiently and can be used to build a more extensive neuromorphic system. Here, we address a fundamental problem: can we efficiently perform artificial intelligence applications with ONNs?
Authors: Madeleine Abernot, Thierry Gil, Manuel Jiménez, Juan Núñez, María J. Avedillo, Bernabé Linares-Barranco, Théophile Gonos, Tanguy Hardelin and Aida Todri-Sanial