集成的光子3D张量处理引擎
1State Key Laboratory of Photonics and Communications, School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai, 200240, China.
Light, science & applications
|March 6, 2026
概括
本研究介绍了一种集成的光子3D张量处理引擎 (3D-TPE),通过在光学领域进行完全计算来加速深度学习. 这种新的方法减少了3D卷积神经网络的开销和提高了效率.
科学领域:
- 光子学 是一个光子学.
- 光学计算是指光学计算的应用.
- 深度学习的加速.
背景情况:
- 主流光子加速器仅限于2D矩阵向量乘法,需要复杂的数据重塑用于3D任务.
- 在当前硬件上实施3D卷积神经网络需要大量的内存和时间开销,这是由于电域处理和同步.
- 外部电子时钟增加了光子加速器中通道同步的系统复杂性.
研究的目的:
- 为高效的3D卷积神经网络计算提出和演示一个集成的光子3D张量处理引擎 (3D-TPE).
- 为了实现光学域数据缓存,通道同步和计算,从而减少内存使用量和系统复杂性.
- 为新兴的人工智能应用程序至关重要的高阶张量运算推进光学计算解决方案.
主要方法:
- 开发了一种3D-TPE,利用时间,波长和空间的交联调制.
- 集成了一个光学调节延迟线 (OTDL) 芯片,用于光学缓存和同步到200GHz.
- 采用基于双合微环共振器 (MRR) 的横杆芯片进行光学计算.
主要成果:
- 证明了3D-TPE在10GHz至30GHz的时钟频率上的处理能力.
- 在20 GHz的概念验证LiDAR 3D点云图像识别任务中实现了97.06%的识别精度.
- 展示了光学领域操作的潜力,与传统方法相比,减少了内存和时间的开销.
结论:
- 拟议的3D-TPE为光学领域的高阶张量卷曲提供了简化和高效的解决方案.
- 这项技术对加速人工智能应用在自动驾驶,医疗保健和虚拟现实等领域具有重大意义.
- 综合光子方法为下一代硬件加速器为复杂的深度学习模型铺平了道路.
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