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基于空间光谱超多重复平行衍射的单射矩阵-矩阵光子处理器.

Chao Luan1, Ronald Davis Iii2, Zaijun Chen3

  • 1Research Laboratory of Electronics, MIT, Cambridge, MA, USA. chaoluan@mit.edu.

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此摘要是机器生成的。

一个新的光学神经网络 (ONN) 处理器使用超复杂化来实现高平行性和能源效率. 这种可扩展的设计加速了超低光能消耗的深度学习任务,使下一代计算成为可能.

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科学领域:

  • 光电学和光子学的光电子和光子学.
  • 人工智能 硬件 硬件
  • 计算机工程 计算机工程

背景情况:

  • 不断增长的数据需求需要高速,节能计算硬件.
  • 模拟光学神经网络 (ONN) 处理器在带宽和功耗方面具有优势.
  • 现有的ONN架构在计算并行性和可扩展性方面存在局限性.

研究的目的:

  • 为了引入一个新的空间-波长-时间超复杂的ONN处理器.
  • 在并行性和可扩展性方面解决当前ONN处理器的局限性.
  • 为了实现深度学习的大规模,高性能光学张量处理.

主要方法:

  • 基于并行衍射束路由的超多重复ONN架构的开发.
  • 一个16x16平行衍射束路由系统的演示.
  • 实现单射矩阵-矩阵乘法用于加速神经网络.

主要成果:

  • 实现了一个大规模的 (16x16-by-16x16) 光学张力处理器,具有高平行度 (4096 MACs/shot) 和高速度 (2 Gsa/s).
  • 在光学领域使用卷积神经网络 (CNN) 和深度神经网络 (DNN) 证明了基准图像识别.
  • 在96.4%的分类准确度下运行,光能消耗极低 (约20 attojoules/MAC).

结论:

  • 拟议的超多重复的ONN处理器架构是可行的大规模实施.
  • 该系统支持广泛的光谱和空间带宽,使光学计算能够取得重大进展.
  • 这项技术为下一代深度学习应用程序的高效,大规模光学计算铺平了道路.