A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser
A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser
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DOI：
10.1088/2515-7647/abf6bd
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发表时间：
2021-04-01
影响因子：
3.8
通讯作者：
Brunner, Daniel
中科院分区：
文献类型：
--
作者：
Porte, Xavier;Skalli, Anas;Brunner, Daniel
神经网络是我们这个时代颠覆性的计算概念之一。然而，它们与经典的算法计算有根本的不同。这些差异给使用当前计算基底的神经网络计算带来了同样根本、严峻和相关的挑战。神经网络迫切需要整个处理器的并行性以及内存和算术的共置，即超越冯·诺依曼架构。并行性尤其使光子学成为一个非常有前途的平台，但到目前为止，可扩展和可集成的概念还很少。在这里，我们首次演示了如何通过半导体多模激光器复杂光学近场的空间复用神经元来实现完全并行和完全实现的光子神经网络。离散空间采样定义类似于大面积垂直腔表面发射激光器表面上的 90 个节点，该激光器以光学方式注入人工神经网络输入信息。重要的是，所有神经网络连接都是在硬件中实现的，我们的处理器无需经过预处理或后处理即可生成结果。输入和输出权重分别通过多模光纤和数字微镜阵列的复杂传输矩阵实现。我们训练读出权重来执行2位标头识别、2位异或逻辑函数和2位数模转换，并获得
Neural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing. These differences result in equally fundamental, severe and relevant challenges for neural network computing using current computing substrates. Neural networks urge for parallelism across the entire processor and for a co-location of memory and arithmetic, i.e. beyond von Neumann architectures. Parallelism in particular made photonics a highly promising platform, yet until now scalable and integratable concepts are scarce. Here, we demonstrate for the first time how a fully parallel and fully implemented photonic neural network can be realized by spatially multiplexing neurons across the complex optical near-field of a semiconductor multimode laser. Discrete spatial sampling defines similar to 90 nodes on the surface of a large-area vertical cavity surface emitting laser that is optically injected with the artificial neural networks input information. Importantly, all neural network connections are realized in hardware, and our processor produces results without pre- or post-processing. Input and output weights are realized via the complex transmission matrix of a multimode fiber and a digital micro-mirror array, respectively. We train the readout weights to perform 2-bit header recognition, a 2-bit XOR logical function and 2-bit digital to analog conversion, and obtain