数字-模拟混合矩阵乘法处理器用于光学神经网络
Xiansong Meng1, Deming Kong2, Kwangwoong Kim3
1DTU Electro, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark.
Nature communications
|August 12, 2025
概括
光学神经网络 (ONN) 为人工智能实现了更高的计算效率. 一个新的混合光学处理器提供了高数值精度,克服了模拟设计的局限性,以获得更好的AI性能.
科学领域:
- 光子学是指光子学的使用方法.
- 人工智能的人工智能
- 计算机工程 计算机工程
背景情况:
- 光学神经网络 (ONN) 为人工智能 (AI) 提供了潜在的计算效率增长.
- 在ONN中现有的模拟矩阵向量乘法 (MVM) 由于电光处理中的噪声而受到有限的数值精度的影响.
- 这种精度限制阻碍了ONN在复杂的人工智能任务中的性能.
研究的目的:
- 为 ONNs 提出并展示一种新的数字-模拟混合MVM架构.
- 在不损害效率的情况下,在光学计算中实现高数值精度.
- 为了验证这种混合架构在图像处理和对象检测中的实际应用.
主要方法:
- 一个概念验证混合光学处理器 (HOP) 的制造.
- 测试高清图像处理的HOP,评估像素误差率和信号噪声比.
- 在MNIST数字识别任务中评估准确性.
- 将HOP应用到你只看一次 (YOLO) 对象检测.
主要成果:
- 制造出来的HOP实现了16位的数字精度.
- 在图像处理中显示的像素误差率为1.8 × 10-3在18.2dB的信号噪声比下.
- 在MNIST数字识别中没有显示精度损失.
- 证实了数字精度在YOLO中对高可靠性物体检测的关键作用.
结论:
- 数字模拟混合MVM架构成功提高了ONN的数值精度.
- 这种方法克服了纯粹模拟光学计算的固有局限性.
- 混合光学计算概念适用于各种光子MVM实现,为准确的光学AI铺平了道路.
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