为光子神经网络解决光学调制器非线性问题
Peter Seigo Kincaid1, Nicola Andriolli2, Giampiero Contestabile1
1Scuola Superiore Sant'Anna, Via G. Moruzzi 1, Pisa, 56124, Italy.
Communications engineering
|March 27, 2025
概括
模拟光子学提供高速的神经形态计算,但面临噪声和扭曲. 这项研究分析和最小化调制器中的非线性,发现适合特定机器学习架构的马赫-泽恩德干扰仪.
科学领域:
- 神经形态计算是一种神经形态计算.
- 模拟光子学 模拟光子学
- 综合光子学 综合光子学
背景情况:
- 模拟光子系统为神经形态计算提供高计算速度和低功耗.
- 模拟系统中的非线性扭曲和噪声限制了信号分辨率和整体功能.
研究的目的:
- 开发一种方法来分析和最小化调制器的光功率传输函数中的非线性.
- 为了比较不同类型的调制器 (马赫-泽恩德干扰仪,微环调制器,环辅助马赫-泽恩德干扰仪) 以及它们适用于模拟光子处理器的适用性.
- 评估基于多重复合技术的机器学习应用程序的三个模拟光子处理器架构.
主要方法:
- 在通用调制器的光功率传输函数中分析非线性.
- 马赫-泽恩德干扰仪,微环调制器和环辅助马赫-泽恩德干扰仪的比较性能评估.
- 将分析应用于用于机器学习的波长,空间和时间分割复杂化架构的分析.
主要成果:
- 分析和最小化调制器非线性的一种方法被介绍和应用.
- 尽管最大分辨率较低,但马赫-泽恩德干扰仪在特定架构的稳定性和功耗方面表现出卓越的平衡.
- 该研究确定了模拟光子处理器的最佳设计和操作条件.
结论:
- 马赫-泽恩德干扰仪是机器学习中空间和时间分割复杂化模拟光子处理器架构的平衡选择.
- 将非线性最小化对于提高模拟光子系统的性能和可靠性至关重要.
- 提出的分析方法有助于明智设计下一代神经形态计算硬件.
相关概念视频
01:37Neural Regulation
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
01:25Neural Circuits
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...