Engineering Creativity Award: Optical and Electronic Implementations of Neural Models

工程创意奖：神经模型的光学和电子实现

• 批准号: 8811586
• 负责人: Amnon Yariv
• 金额: $ 8.94万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-09-01 至 1992-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8811586&HistoricalAwards=false
• 关键词: Engineering; Creativity; Award; Optical; Electronic

This research project is directed to exploring the possibilities of implementing large parallel generic neural network architectures using both silicon devices and optical technology. The underlying principle of the new architectures is to take advantage of the fact that signal processing in silicon ins an advanced and mature technology and to incorporate optics where silicon fails, namely, the interconnectivity problem. These new architectures make possible the construction of fully integrated, alterable networks with 1000 neurons using existing technologies. The next important breakthrough will be to implement a complete learning structure with local memory on a single device. Large, fully parallel architectures that incorporate a simple learning algorithm are delineated and their initial design specified.

这个研究项目旨在探索 实现大型并行通用神经网络 使用硅器件和光学技术的架构。 新架构的基本原则是 硅中的信号处理的优点在于， 先进和成熟的技术，并结合光学， 硅失效，即互连问题。 这些新 架构使得完全集成的， 使用现有技术的1000个神经元的可变网络。 下一个重要突破将是实施一项完整的 在单个设备上具有本地存储器的学习结构。 大号、 融入简单学习的完全并行架构 算法划定和他们的初始设计指定。