Resonance for Analog Recurrent Neural Network
Yurui Qu, Ming Zhou, Erfan Khoram, Nanfang Yu, Zongfu Yu
Abstract
There is a strong interest in using physical waves for artificial neural computing because of their unique advantages in fast speed and intrinsic parallelism. Resonance, as a ubiquitous feature across many wave systems, is a natural candidate for analog computing in temporal signals. We demonstrate that resonance can be used to construct stable and scalable recurrent neural networks. By including resonators with different lifetimes, the computing system develops both short-term and long-term memories simultaneously.
Topics & Concepts
Computer scienceScalabilityArtificial neural networkResonatorConstruct (python library)Reservoir computingAnalog computerFeature (linguistics)Resonance (particle physics)Recurrent neural networkNatural computingArtificial intelligencePhysicsElectrical engineeringOptoelectronicsEngineeringPhilosophyProgramming languageDatabaseParticle physicsLinguisticsNeural Networks and Reservoir ComputingNeural Networks and ApplicationsPhotonic and Optical Devices