  While analog neural network (NN) accelerators promise massive energy and time
savings, an important challenge is to make them robust to static fabrication
error. Present-day training methods for programmable photonic interferometer
circuits, a leading analog NN platform, do not produce networks that perform
well in the presence of static hardware errors. Moreover, existing hardware
error correction techniques either require individual re-training of every
analog NN (which is impractical in an edge setting with millions of devices),
place stringent demands on component quality, or introduce hardware overhead.
We solve all three problems by introducing one-time error-aware training
techniques that produce robust NNs that match the performance of ideal hardware
and can be exactly transferred to arbitrary highly faulty photonic NNs with
hardware errors up to 5x larger than present-day fabrication tolerances.
