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arXiv 2108.04819physics.opticscs.ET
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量化硅光子神经网络中的功率使用

Quantifying power use in silicon photonic neural networks

Alexander N. Tait

摘要:

由于传统和非传统电子架构面临的挑战性效率限制,基于光子学的信息处理器吸引了新的兴趣。研究团体还没有确定的技术来描述这类信息处理器的性能。光子系统不同于电子系统,因此现有的计算机性能测量概念不一定适用。在这篇手稿中,我们试图用最先进的和未来的硬件来量化光子神经网络的功率使用。我们得出的缩放定律,物理限制,和新的平台性能指标。我们发现,整体性能是regime-like,这意味着光子处理器的能效特性可以完全描述不少于7个性能数字。这些分析策略的引入为硅光子神经网络的定量路线图和商业价值分配提供了急需的基础。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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