  Photonic tensor cores (PTCs) are essential building blocks for optical
artificial intelligence (AI) accelerators based on programmable photonic
integrated circuits. PTCs can achieve ultra-fast and efficient tensor
operations for neural network (NN) acceleration. Current PTC designs are either
manually constructed or based on matrix decomposition theory, which lacks the
adaptability to meet various hardware constraints and device specifications. To
our best knowledge, automatic PTC design methodology is still unexplored. It
will be promising to move beyond the manual design paradigm and "nurture"
photonic neurocomputing with AI and design automation. Therefore, in this work,
for the first time, we propose a fully differentiable framework, dubbed ADEPT,
that can efficiently search PTC designs adaptive to various circuit footprint
constraints and foundry PDKs. Extensive experiments show superior flexibility
and effectiveness of the proposed ADEPT framework to explore a large PTC design
space. On various NN models and benchmarks, our searched PTC topology
outperforms prior manually-designed structures with competitive matrix
representability, 2-30x higher footprint compactness, and better noise
robustness, demonstrating a new paradigm in photonic neural chip design. The
code of ADEPT is available at https://github.com/JeremieMelo/ADEPT using the
https://github.com/JeremieMelo/pytorch-onn (TorchONN) library.
