通过调制和权重微波振器进行紧,可重新配置和可扩展的光子神经元
Weipeng Zhang1,2, Yuxin Wang1, Joshua C Lederman1
1Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544 NJ USA.
概括
这项研究介绍了一种紧的光子神经元,使用微环共振器进行可扩展的神经形态计算. 这种新的设计实现了高级AI任务的高计算密度和低功耗.
科学领域:
- 神经形态工程的神经形态工程
- 光子学 是一个光子学.
- 集成光学 集成光学 集成光学
背景情况:
- 神经形态光子学中的可扩展性挑战包括制造,光谱对齐和能源效率.
- 现有的设计往往需要复杂的光学设置和大量的功率.
研究的目的:
- 开发一个大规模的,紧的,可重新配置的光子神经元.
- 为了克服制造公差,光谱对齐和神经形态系统调能量的局限性.
主要方法:
- 使用微环共振器,同时执行调制和加权.
- 在一个单一的设备中使用载波和热调.
- 集成了一个短的电反路径,用于反复运行和内存功能.
主要成果:
- 证明了一种光子神经元,其足迹减少,光谱对齐要求放松.
- 实现了高计算密度 (4.67 TOPS/s/mm2) 和低功耗 (0.186 mW 每个元素).
- 展示了空间 (3x3卷积,<5%误差) 和时间计算 (金融时间序列预测).
结论:
- 微环共振器银行为大规模的神经形态光子系统提供了可扩展的构建模块.
- 拟议的架构提供了紧尺寸,低功耗和功能灵活性的良好平衡.
- 这一进步为更高效,更强大的光子AI硬件铺平了道路.
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