集成的多操作光学神经元,用于可扩展和硬件高效的深度学习.
Chenghao Feng1,2, Jiaqi Gu2,3, Hanqing Zhu2
1Microelectronics Research Center, The University of Texas at Austin, Austin, TX 78758, USA.
Nanophotonics (Berlin, Germany)
|December 5, 2024
概括
本研究介绍了一种用于光学神经网络 (ONN) 的新型多操作光学神经元 (MOON). 这项创新大大降低了光子张量核 (PTC) 中的面积成本和传播损失,以实现高效的神经形态计算.
科学领域:
- 光子学 是一个光子学.
- 神经形态计算是一种神经形态计算.
- 光学神经网络是指光学神经网络.
背景情况:
- 集成光子张量核 (PTC) 面临着由于单操作元调制器的巨大面积成本和高传播损失的挑战.
- 在光学神经网络 (ONN) 中高效地实现大张量运算对于推进神经形态计算至关重要.
研究的目的:
- 提出一个可扩展和高效的光学点产品引擎,使用多操作的光子设备.
- 为了证明多操作光学神经元 (MOON) 在图像识别任务中的有效性.
主要方法:
- 基于多操作的马赫-泽恩德干扰仪 (MOMZI) 的多操作光学神经元 (MOON) 的开发.
- 基于MOMZI的ONN用于图像识别的实验演示.
- 性能分析比较基于MOMZI的PTC与基于MZI的单操作对应产品.
主要成果:
- 基于MOMZI的ONN在4位电压控制的街景房屋号码 (SVHN) 数据集上实现了85.89%的准确性.
- 基于128x128 MOMZI的PTC在传播损失,光学延迟和设备足迹方面表现出比单操作子MZI更好的性能.
- 保持了可比的矩阵表达性,效率显著提高.
结论:
- 拟议的MOON,与MOMZI实现,为光子张量核提供了一个可扩展和高效的解决方案.
- 这种方法显著减少了硬件开销,并提高了大规模神经形态计算应用的性能.
- 基于MOMZI的设计代表了人工智能硬件集成光子设备的重大进步.
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