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.

翻译：光子学是一种有前景的深度神经网络加速技术，它利用光互连降低数据移动能耗，并实现低能耗、高吞吐量的光学模拟计算。要在完整系统（加速器+动态随机存取存储器）中实现这些优势，设计者必须确保电气、光学、模拟和数字域之间的转换收益超过域间数据转换的成本。设计者还需考虑系统级能耗成本，例如从动态随机存取存储器获取数据的能耗。由于数据转换和访问动态随机存取存储器可能消耗大量能量，因此需要一种能够对完整系统进行建模的工具来评估和探索光子学系统空间。在本工作中，我们证明了存算一体与光子学之间的相似性使我们能够利用存算一体系统建模工具精确构建光子学系统模型。将建模工具引入光子学领域可实现全系统背景下的光子学研究评估、快速设计空间探索、协同设计及系统间比较。通过使用我们的开源模型，我们发现跨域转换和动态随机存取存储器可能消耗光子学系统的很大一部分能量。随后我们展示了能减少转换次数和动态随机存取存储器访问的优化方案，使光子学系统能效提升高达3倍。