  Silicon-photonics-based optical neural network (ONN) is a promising hardware
platform that could represent a paradigm shift in efficient AI with its
CMOS-compatibility, flexibility, ultra-low execution latency, and high energy
efficiency. In-situ training on the online programmable photonic chips is
appealing but still encounters challenging issues in on-chip implementability,
scalability, and efficiency. In this work, we propose a closed-loop ONN on-chip
learning framework L2ight to enable scalable ONN mapping and efficient in-situ
learning. L2ight adopts a three-stage learning flow that first calibrates the
complicated photonic circuit states under challenging physical constraints,
then performs photonic core mapping via combined analytical solving and
zeroth-order optimization. A subspace learning procedure with multi-level
sparsity is integrated into L2ight to enable in-situ gradient evaluation and
fast adaptation, unleashing the power of optics for real on-chip intelligence.
Extensive experiments demonstrate our proposed L2ight outperforms prior ONN
training protocols with 3-order-of-magnitude higher scalability and over 30X
better efficiency, when benchmarked on various models and learning tasks. This
synergistic framework is the first scalable on-chip learning solution that
pushes this emerging field from intractable to scalable and further to
efficient for next-generation self-learnable photonic neural chips. From a
co-design perspective, L2ight also provides essential insights for
hardware-restricted unitary subspace optimization and efficient sparse
training. We open-source our framework at
https://github.com/JeremieMelo/L2ight.
