Polariton lattices as binarized neuromorphic networks

TL;DR

This paper presents a new neuromorphic network architecture using polariton condensate lattices that perform binary operations, enabling efficient, scalable, and parallel processing for tasks like image and voice recognition, with high accuracy.

Contribution

The paper introduces a polariton lattice-based binary neuromorphic network architecture that improves computational efficiency and scalability over traditional neural networks.

Findings

Achieved up to 97.5% accuracy on MNIST image recognition.

Surpassed HMM-GMM in voice recognition accuracy with 68%.

Demonstrated potential for fast, parallel processing in neuromorphic systems.

Abstract

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 framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication. The binary neuron switching mechanism, driven by the nonlinear repulsion through the excitonic component of polaritons, offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing, enhancing computational speed compared to sequential or pulse-coded binary systems. The system's performance was evaluated using diverse datasets, including the MNIST dataset for image recognition and the Speech Commands dataset for voice recognition tasks. In both scenarios, the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems. For image recognition, this is evidenced by an impressive predicted classification accuracy of up to 97.5%. In voice recognition, the system achieved a classification accuracy of about 68\% for the ten-class subset, surpassing the performance of conventional benchmark, the Hidden Markov Model with Gaussian Mixture Model.