Integrated photonic neural networks (PNNs) are at the forefront of AI computing, leveraging light’s unique properties, such as large bandwidth, low latency, and potentially low power consumption. Nevertheless, the integrated optical components are inherently sensitive to external disturbances, thermal interference, and various device imperfections, which detrimentally affect computing accuracy and reliability. Conventional solutions use complicated control methods to stabilize optical devices and chip, which result in high hardware complexity and are impractical for large-scale PNNs. To address this, we propose a training approach to enable control-free, accurate, and energy-efficient photonic computing without adding hardware complexity. The core idea is to train the parameters of a physical neural network towards its noise-robust and energy-efficient region. Our method is validated on different integrated PNN architectures and is applicable to solve various device imperfections in thermally tuned PNNs and PNNs based on phase change materials. A notable 4-bit improvement is achieved in micro-ring resonator-based PNNs without needing complex device control or power-hungry temperature stabilization circuits. Additionally, our approach reduces the energy consumption by tenfold. This advancement represents a significant step towards the practical, energy-efficient, and noise-resilient implementation of large-scale integrated PNNs.

中文翻译：

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通过硬件感知训练和修剪实现无控制且高效的集成光子神经网络


集成光子神经网络 (PNN) 处于人工智能计算的最前沿，利用光的独特特性，例如大带宽、低延迟和潜在的低功耗。然而，集成光学元件本质上对外部干扰、热干扰和各种器件缺陷很敏感，这会对计算精度和可靠性产生不利影响。传统的解决方案使用复杂的控制方法来稳定光学器件和芯片，这导致硬件复杂度很高，并且对于大规模 PNN 来说是不切实际的。为了解决这个问题，我们提出了一种训练方法，可以在不增加硬件复杂性的情况下实现无控制、准确且节能的光子计算。核心思想是将物理神经网络的参数训练到其抗噪声和节能区域。我们的方法在不同的集成 PNN 架构上进行了验证，适用于解决热调谐 PNN 和基于相变材料的 PNN 中的各种器件缺陷。基于微环谐振器的 PNN 实现了显着的 4 位改进，无需复杂的设备控制或耗电的温度稳定电路。此外，我们的方法将能源消耗降低了十倍。这一进步代表着向大规模集成 PNN 的实用、节能和抗噪声实施迈出了重要一步。