Characterization of nonlinear spin-wave interference by reservoir-computing metrics
Ádám Papp, György Csaba, Wolfgang Porod
Abstract
We study the computational potential of a spin-wave (SW) substrate by applying two metrics known from reservoir computing. At low intensities, SW scatterers can perform linear operations, while at higher intensities, nonlinear phenomena dominate, possibly enabling high-function, general-purpose computing. The transition between the linear and nonlinear regimes can be quantified by the intensity-dependent kernel rank (KR) and generalization rank (GR). The KR and GR metrics prove that the SW substrate displays the nonlinearities required for computing and give recipes for device designs that utilize nonlinearity.
Topics & Concepts
Nonlinear systemReservoir computingGeneralizationInterference (communication)Rank (graph theory)Kernel (algebra)Computer scienceSubstrate (aquarium)MathematicsPhysicsMathematical analysisArtificial intelligenceArtificial neural networkTelecommunicationsDiscrete mathematicsGeologyQuantum mechanicsCombinatoricsChannel (broadcasting)Recurrent neural networkOceanographyNeural Networks and Reservoir ComputingAdvanced Memory and Neural ComputingQuantum and electron transport phenomena