多波长光子神经网络的在线培训和修剪
Jiawei Zhang1, Weipeng Zhang1, Tengji Xu2
1Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA.
Nanophotonics (Berlin, Germany)
|December 22, 2025
概括
本研究介绍了一种对光子神经网络 (PNN) 的在线训练方法,该方法可以适应设备的变化. 这种方法通过优化PNN准确性和减少调整功耗来提高AI计算可扩展性和能源效率.
科学领域:
- 光子学 是一个光子学.
- 人工智能的人工智能
- 集成电路 集成电路
背景情况:
- 兼容CMOS的光子集成电路 (PIC) 对AI计算至关重要.
- 基于微环共振器 (MRR) 的光子神经网络 (PNN) 通过波长分割复杂化 (WDM) 提供紧的足迹和高效的互连.
- 由于制造工艺变化 (FPV) 和环境变化导致的MRR共振波长变化限制了可扩展性和能源效率.
研究的目的:
- 为基于MRR的PNN开发一个在线培训和修剪方法.
- 为了解决由FPV和热波动引起的MRR共振波长变化.
- 为了同时优化PNN准确度,并减少MRR调的功耗.
主要方法:
- 实施PNN的在线培训和修剪策略.
- 在损失函数中纳入了功率意识的修剪术语.
- 使用Iris数据集进行了芯片上的实验.
主要成果:
- 在26-40°C的温度范围内保持了90%以上的分类准确度.
- 通过修剪实现了44.7%的调功率减少.
- 在较大的数据集上证明了数量级的功率减少.
结论:
- 拟议的方法显著提高了基于MRR的模拟光子处理器的可扩展性和能效.
- 解决芯片对芯片的变化,并最大限度地减少集成光子系统的功率需求.
- 支持用于神经网络,光子切换,LiDAR和射频束成形等应用的大规模PIC.
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