量子:对光学神经元的量子化意识训练
Hasindu Kariyawasam1, Ramith Hettiarachchi1, Quansan Yang2,3
1Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA.
Research square
|April 8, 2024
概括
我们开发了一个基于物理的培训框架,用于设计带有量子化光学神经元的光学处理器. 这种方法确保了强大的性能,尽管制造精度限制,使先进的3D制造光学计算.
科学领域:
- 光子学和光学计算的应用
- 机器学习 硬件加速 机器学习 硬件加速
- 纳米制造和材料科学 材料科学
背景情况:
- 光学处理器提供高速,高维线性计算.
- 3D微型制造技术的进步使复杂的光学处理器成为可能.
- 制造精度有限导致光神经元参数的量化,导致模型不匹配.
研究的目的:
- 开发一个培训框架,以考虑光学处理器设计中的物理限制.
- 为了使光学处理器的设计具有量化可学习参数在预定义的精度水平.
- 为了确保光学处理器的强大性能,尽管制造限制.
主要方法:
- 提出了一个基于物理的量子化意识培训框架.
- 将身体限制直接整合到培训过程中.
- 在光学处理器设计中利用衍射网络.
主要成果:
- 展示了用于基于物理的任务的最先进的光学处理器设计.
- 成功解决了光学神经元中量子化可学习参数的挑战.
- 通过在训练期间考虑物理约束,实现了强大的设计.
结论:
- 拟议的框架使3D制造的光学处理器具有量化参数的可靠设计成为可能.
- 这项工作为光学计算硬件的未来进步奠定了基础.
- 基于物理的培训对于克服光学神经网络的制造局限性至关重要.
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