  Due to challenging efficiency limits facing conventional and unconventional
electronic architectures, information processors based on photonics have
attracted renewed interest. Research communities have yet to settle on
definitive techniques to describe the performance of this class of information
processors. Photonic systems are different from electronic ones, so the
existing concepts of computer performance measurement cannot necessarily apply.
In this manuscript, we attempt to quantify the power use of photonic neural
networks with state-of-the-art and future hardware. We derive scaling laws,
physical limits, and new platform performance metrics. We find that overall
performance is regime-like, which means that energy efficiency characteristics
of a photonic processor can be completely described by no less than seven
performance numbers. The introduction of these analytical strategies provides a
much needed foundation for quantitative roadmapping and commercial value
assignment for silicon photonic neural networks.
