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 ∼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 and 2.9 10⁻² error rates for digit recognition and XOR, respectively. Finally, the digital to analog conversion can be realized with a standard deviation of only 5.4 10⁻². Crucially, our proof-of-concept system is scalable to much larger sizes and to bandwidths in excess of 20 GHz.

神经网络是我们这个时代的颠覆性计算概念之一。然而，它们从根本上不同于经典的算法计算。这些差异导致使用当前计算基板的神经网络计算面临同样基本、严峻和相关的挑战。神经网络要求整个处理器的并行性以及内存和算术的协同定位，即超越冯诺依曼架构。并行性尤其使光子学成为一个很有前途的平台，但直到现在，可扩展和可集成的概念还很少见。在这里，我们首次展示了如何完全并行和完全实施光子神经网络可以通过跨半导体多模激光器的复杂光学近场空间复用神经元来实现。离散空间采样在大面积垂直腔面发射激光器的表面上定义了约 90 个节点，该激光器通过人工神经网络输入信息进行光学注入。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传输矩阵实现。我们训练读出权重以执行 2 位标头识别、2 位 XOR 逻辑函数和 2 位数模转换，并获得2.9 10 ^-2分别为数字识别和 XOR 的错误率。最后，可以以仅5.4 10 ^-2的标准偏差实现数模转换。至关重要的是，我们的概念验证系统可扩展到更大的尺寸和超过 20 GHz 的带宽。