Training a neural network with exciton-polariton optical nonlinearity
In contrast to software simulations of neural networks, hardware implementations have often limited or no tunability. While such networks promise great improvements in terms of speed and energy efficiency, their performance is limited by the difficulty to apply efficient training. We propose and realize experimentally an optical system where highly efficient backpropagation training can be applied through an array of highly nonlinear, non-tunable nodes. The system includes exciton-polariton nodes realizing nonlinear activation functions. We demonstrate a high classification accuracy in the MNIST handwritten digit benchmark in a single hidden layer system.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Fourier Neural Operator Approach for Modelling Exciton-Polariton Condensate Systems
A plethora of next-generation all-optical devices based on exciton-polaritons have been proposed in latest years, including prototypes of transistors, switches, analogue quantum simulators and others. However, for such s…
GPUPolariton lattices as binarized neuromorphic networks
We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through non-resonant optical pumping. The network employs a binary fram…
Computational EfficiencyHandwritten Digit RecognitionGenerative modelling powered by room-temperature polariton condensates
Generative modelling requires efficient stochastic nonlinear transformations and physical platforms that can naturally realise them. We experimentally demonstrate that nonlinear optical systems operating in the strong li…
Exploring Structural Nonlinearity in Binary Polariton-Based Neuromorphic Architectures
This study investigates the performance of a binarized neuromorphic network leveraging polariton dyads, optically excited pairs of interfering polariton condensates within a microcavity to function as binary logic gate n…
image-classificationImage ClassificationNear-Equilibrium Propagation training in nonlinear wave systems
Backpropagation learning algorithm, the workhorse of modern artificial intelligence, is notoriously difficult to implement in physical neural networks. Equilibrium Propagation (EP) is an alternative with comparable effic…