  Photonics is a promising technology to accelerate Deep Neural Networks as it
can use optical interconnects to reduce data movement energy and it enables
low-energy, high-throughput optical-analog computations.
  To realize these benefits in a full system (accelerator + DRAM), designers
must ensure that the benefits of using the electrical, optical, analog, and
digital domains exceed the costs of converting data between domains. Designers
must also consider system-level energy costs such as data fetch from DRAM.
Converting data and accessing DRAM can consume significant energy, so to
evaluate and explore the photonic system space, there is a need for a tool that
can model these full-system considerations.
  In this work, we show that similarities between Compute-in-Memory (CiM) and
photonics let us use CiM system modeling tools to accurately model photonics
systems. Bringing modeling tools to photonics enables evaluation of photonic
research in a full-system context, rapid design space exploration, co-design,
and comparison between systems.
  Using our open-source model, we show that cross-domain conversion and DRAM
can consume a significant portion of photonic system energy. We then
demonstrate optimizations that reduce conversions and DRAM accesses to improve
photonic system energy efficiency by up to 3x.
