  Diffractive optical neural networks (DONNs) have attracted lots of attention
as they bring significant advantages in terms of power efficiency, parallelism,
and computational speed compared with conventional deep neural networks (DNNs),
which have intrinsic limitations when implemented on digital platforms.
However, inversely mapping algorithm-trained physical model parameters onto
real-world optical devices with discrete values is a non-trivial task as
existing optical devices have non-unified discrete levels and non-monotonic
properties. This work proposes a novel device-to-system hardware-software
codesign framework, which enables efficient physics-aware training of DONNs
w.r.t arbitrary experimental measured optical devices across layers.
Specifically, Gumbel-Softmax is employed to enable differentiable discrete
mapping from real-world device parameters into the forward function of DONNs,
where the physical parameters in DONNs can be trained by simply minimizing the
loss function of the ML task. The results have demonstrated that our proposed
framework offers significant advantages over conventional quantization-based
methods, especially with low-precision optical devices. Finally, the proposed
algorithm is fully verified with physical experimental optical systems in
low-precision settings.
