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通过基于裂变的宽带频率生成的神经形态计算.
Bennet Fischer1,2, Mario Chemnitz1,2, Yi Zhu1
1Institut National de la Recherche Scientifique - Énergie, Matériaux et Télécommunications, 1650 Blvd. Lionel-Boulet, Varennes, Quebec, J3X1S2, Canada.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 3, 2023
概括
本研究介绍了使用光纤模拟神经网络的神经形态波计算. 这种方法提供了节能,可扩展的数字神经网络模拟,通过系统非线性来提高性能.
科学领域:
- 光子学和神经形态工程学
- 光学计算架构的架构是光学计算的架构.
背景情况:
- 传统的计算机架构面临性能限制,引发了对脑启发硬件的兴趣.
- 光学解决方案提供了能源效率和速度,但集成神经模拟的非线性节点是具有挑战性的,并限制了可扩展性.
研究的目的:
- 提出一种新的神经形态波计算范式,用于节能信息处理.
- 在光纤集成系统中使用非线性光学相互作用来演示数字神经网络的模拟.
主要方法:
- 使用宽带频率转换通过连贯的高阶单离子裂变在单模光纤.
- 采用秒脉冲的相位编码和可解释的系统状态的频率选择/权重.
- 在一个紧的,全纤维集成的设置中进行实验.
主要成果:
- 证明了数字神经网络的节能模拟.
- 随着系统非线性度的增加,观察到增强的计算性能.
- 在一个紧的,纤维集成的实验设置中验证了这个概念.
结论:
- 纤维宽带频率生成为传统基于节点的脑启发硬件提供了可行的替代方案.
- 这种方法可以使用最小的光学硬件实现节能,可扩展和可靠的计算.
- 神经形态波计算通过利用非线性光学动态来挑战传统设计.
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