用光实现神经网络计算，速度更快能耗更低。
Optical Echo State Network Reservoir Computing
- 利用光的非线性特性实现无需测量的激活运算
- 模拟测试显示性能媲美传统软件方法
- 适合需要高速低耗的机器学习场景
我们提出一种新型光学回声状态网络（ESN）设计，可实现任意类型的光学回声状态网络，具备灵活的光矩阵乘法和非线性激活能力。通过利用受激布里渊散射（SBS）的非线性特性，该架构高效实现了无测量的非线性激活，显著降低了计算开销与能耗。全面仿真验证了系统的记忆容量、非线性处理能力和多项式代数运算能力，在关键基准任务上的表现与软件实现的ESN相当。本设计为光学储备池计算提供了一个可行、可扩展且通用的框架，适用于多种机器学习应用。
原文摘要 · Abstract (English)
We propose an innovative design for an optical Echo State Network (ESN), an advanced type of reservoir computer known for its universal computational capabilities. Our design enables an optical implementation of arbitrary ESNs, featuring flexibility in optical matrix multiplication and nonlinear activation. Leveraging the nonlinear characteristics of stimulated Brillouin scattering (SBS), the architecture efficiently realizes measurement-free nonlinear activation. The approach significantly reduces computational overhead and energy consumption compared to traditional software-based methods. Comprehensive simulations validate the system's memory capacity, nonlinear processing strength, and polynomial algebra capabilities, showcasing performance comparable to software ESNs across key benchmark tasks. Our design establishes a feasible, scalable, and universally applicable framework for optical reservoir computing, suitable for diverse machine learning applications.
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