Architecture-Level Modeling of Photonic Deep Neural Network Accelerators
<|reference_start|>Architecture-Level Modeling of Photonic Deep Neural Network Accelerators: 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.<|reference_end|>
@article{andrulis2024architecture-level,
    title={Architecture-Level Modeling of Photonic Deep Neural Network Accelerators},
    author={Tanner Andrulis, Gohar Irfan Chaudhry, Vinith M. Suriyakumar, Joel S.
  Emer, Vivienne Sze},
    journal={ISPASS 2024 pp. 307-309},
    year={2024},
    doi={10.1109/ISPASS61541.2024.00040},
    archivePrefix={arXiv},
    eprint={2405.07266},
    primaryClass={cs.ET cs.AR}
}