11 TOPS光子卷积加速器用于光学神经网络
Xingyuan Xu1,2, Mengxi Tan1, Bill Corcoran3
1Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Nature
|January 7, 2021
概括
研究人员开发了一种光学卷积神经网络加速器, 这种新的系统成功地识别出手写的数字, 准确度高达88%, 为更快的AI应用铺平道路.
科学领域:
- 人工智能
- 光学计算
- 机器学习
背景情况:
- 卷积神经网络 (CNN) 是功能提取的强大人工智能工具, 对于计算机视觉和医学诊断等任务至关重要.
- 传统的电子CNN在速度和功耗方面面临限制.
- 通过利用光学带宽,光学神经网络为加速计算提供了一个有前途的途径.
研究的目的:
- 展示一个通用的光学向量卷积加速器.
- 使用光学卷积神经网络实现高速图像识别.
- 探索综合光学系统对复杂人工智能任务的潜力.
主要方法:
- 开发了一种光学向量卷积加速器,每秒运行速度超过10Tera-ops.
- 使用集成的微源来交错时间,波长和空间维度.
- 配置硬件以形成用于图像识别任务的顺序光学卷积神经网络.
主要成果:
- 展示了一种能够处理25万像素图像的光学加速器,
- 使用光学卷积神经网络识别手写数字图像的准确度达到了88%.
- 该系统的运行速度超过每秒10Tera-ops.
结论:
- 开发的光学卷积神经网络加速器为高性能AI提供了可扩展和可训练的平台.
- 这项技术对自动驾驶汽车和实时视频识别等要求高的应用具有显著的潜力.
- 使用微源的综合方法通过交错多个维度实现了高效的处理.
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