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.

翻译：我们提出了一种基于激子-极化子凝聚体晶格的新型神经形态网络架构，该晶格通过非共振光泵浦实现复杂互连与能量供给。该网络采用二值化框架，其中每个神经元在成对耦合凝聚体的空间相干性支持下执行二值运算。这种由极化子弹道传播产生的相干性确保了高效的全网络通信。由极化子激子成分的非线性排斥力驱动的二值神经元切换机制，相较于连续权重神经网络，具有计算效率和可扩展性优势。我们的网络支持并行处理，相比串行或脉冲编码二值系统显著提升了计算速度。我们使用多样化数据集评估了系统性能，包括用于图像识别的MNIST数据集和用于语音识别任务的Speech Commands数据集。在两种场景下，所提出的系统均展现出超越现有极化子神经形态系统的潜力。在图像识别任务中，其预测分类准确率高达97.5%。在语音识别任务中，系统在十分类子集上实现了约68%的分类准确率，超越了传统基准模型——高斯混合隐马尔可夫模型的性能。