一个大规模的光子矩阵处理器,通过电荷积累来实现
Frank Brückerhoff-Plückelmann1,2, Ivonne Bente3,2, Daniel Wendland3,2
1Department of Physics, University of Münster, CeNTech, Heisenberg Str. 11, 48155 Muenster, Germany.
Nanophotonics (Berlin, Germany)
|December 5, 2024
概括
研究人员开发了一种用于人工神经网络 (ANN) 的时间复合光子电路. 这种方法增强了矩阵处理能力,使复杂的AI任务能够进行高效的大规模计算.
科学领域:
- 神经形态光子学 神经形态光子学
- 人工智能硬件是人工智能的硬件.
背景情况:
- 光子电路为人工神经网络 (ANN) 提供了能源和时间的效率,因为它具有高带宽和低损失.
- 扩展当前光子电路以满足现代ANN的需求仍然是一个重大挑战.
研究的目的:
- 为解决ANN现有的光子矩阵处理器的缩放限制.
- 提出和研究一种新的时间复数矩阵处理方案.
主要方法:
- 在ANN中对矩阵大小的概述和与现有的光子矩阵处理器能力的比较.
- 关于使用不连贯光学积累的时间复杂矩阵处理方案的建议和研究.
- 通过1个小时的脉冲实现了98.9%的积累精度.
主要成果:
- 拟议的方案实际上增加了物理光子横杆阵列的尺寸,而无需电气后处理.
- 在时间复合光学积累方面,已证明高积累精度 (98.9%).
- 预计在51.2mm2面积上实现16000×64矩阵的全光矩阵向量乘法的能力.
结论:
- 时间复合的神经形态光子电路架构使ANN能够进行高效的大规模矩阵操作.
- 这种方法可实现每秒超过110万亿次的多倍累积运算,克服了当前的扩展挑战.
相关概念视频
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