Deep neural networks (DNNs) consist of layers of neurons interconnected by synaptic weights. A high bit-precision in weights is generally required to guarantee high accuracy in many applications. Minimizing error accumulation between layers is also essential when building large-scale networks. Recent demonstrations of photonic neural networks are limited in bit-precision due to cross talk and the high sensitivity of optical components (e.g., resonators). Here, we experimentally demonstrate a record-high precision of 9 bits with a dithering control scheme for photonic synapses. We then numerically simulated the impact with increased synaptic precision on a wireless signal classification application. This work could help realize the potential of photonic neural networks for many practical, real-world tasks.

中文翻译：

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硅微环突触可实现超过 9 位精度的光子深度学习

深度神经网络 (DNN) 由通过突触权重互连的神经元层组成。在许多应用中，通常需要高位精度的权重来保证高精度。在构建大规模网络时，最小化层间的错误累积也是必不可少的。由于串扰和光学组件（例如，谐振器）的高灵敏度，最近对光子神经网络的演示在位精度方面受到限制。在这里，我们通过实验证明了具有创纪录的 9 位精度的光子突触抖动控制方案。然后，我们通过增加突触精度对无线信号分类应用程序的影响进行了数值模拟。这项工作可以帮助实现光子神经网络在许多实际任务中的潜力。