论文标题
大型时空光子储层计算机用于图像分类
Large-scale spatiotemporal photonic reservoir computer for image classification
论文作者
论文摘要
我们提出了一个可扩展的光子体系结构，用于实现前馈和复发性神经网络，以从MNIST数据库中执行手写数字的分类。我们的实验利用现成的光学和电子组件目前达到16,384个节点的网络大小。两种网络类型均在带有随机加权输入和隐藏层的储层计算范式中设计。使用各种特征提取技术（例如定向梯度的直方图，分区，Gabor过滤器）以及一个由线性回归和赢家进行的简单培训程序，我们在数值上和实验上都可以在数值上证明，馈电网络允许分类错误率为1％，该错误率是1％的，该错误率是尚有竞争性实施的，并且具有更高的竞争力和竞争性的竞争力。我们还通过明确激活时间动力学来研究数值模拟中的循环网络，并预测馈电配置的性能改善。
We propose a scalable photonic architecture for implementation of feedforward and recurrent neural networks to perform the classification of handwritten digits from the MNIST database. Our experiment exploits off-the-shelf optical and electronic components to currently achieve a network size of 16,384 nodes. Both network types are designed within the the reservoir computing paradigm with randomly weighted input and hidden layers. Using various feature extraction techniques (e.g. histograms of oriented gradients, zoning, Gabor filters) and a simple training procedure consisting of linear regression and winner-takes-all decision strategy, we demonstrate numerically and experimentally that a feedforward network allows for classification error rate of 1%, which is at the state-of-the-art for experimental implementations and remains competitive with more advanced algorithmic approaches. We also investigate recurrent networks in numerical simulations by explicitly activating the temporal dynamics, and predict a performance improvement over the feedforward configuration.
