Optical processors, built with "optical neurons", can efficiently perform high-dimensional linear operations at the speed of light. Thus they are a promising avenue to accelerate large-scale linear computations. With the current advances in micro-fabrication, such optical processors can now be 3D fabricated, but with a limited precision. This limitation translates to quantization of learnable parameters in optical neurons, and should be handled during the design of the optical processor in order to avoid a model mismatch. Specifically, optical neurons should be trained or designed within the physical-constraints at a predefined quantized precision level. To address this critical issues we propose a physics-informed quantization-aware training framework. Our approach accounts for physical constraints during the training process, leading to robust designs. We demonstrate that our approach can design state of the art optical processors using diffractive networks for multiple physics based tasks despite quantized learnable parameters. We thus lay the foundation upon which improved optical processors may be 3D fabricated in the future.

翻译：以“光学神经元”构建的光学处理器能够在光速下高效执行高维线性运算，因此成为加速大规模线性计算的前沿方案。随着微加工技术的进步，此类光学处理器已可实现三维制造，但受限于制造精度。这一限制转化为光学神经元中可学习参数的量化问题，需在光学处理器设计阶段予以处理以避免模型失配。具体而言，光学神经元应在预定义量化精度级别的物理约束下进行训练或设计。针对这一关键问题，我们提出了一种基于物理信息的量化感知训练框架。该方法在训练过程中充分考虑物理约束，从而获得鲁棒性设计。实验表明，尽管存在量化可学习参数，我们的方法仍能利用衍射网络为多个基于物理的任务设计出最先进的光学处理器。这为未来三维制造改进型光学处理器奠定了基础。