Ultra parallel computation using wave interference as means of weighted sum : Holographic neural computing

使用波干涉作为加权和的超并行计算：全息神经计算

• 批准号: 10680339
• 负责人: KUMAZAWA Itsuo
• 金额: $ 1.98万
• 依托单位: Tokyo Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1998
• 资助国家: 日本
• 起止时间: 1998 至 1999
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10680339/
• 关键词: Holographic Memory; Neural Network; Laser; Coherent wave; Parallel computation; Wave interference; Diffraction; Ultrasonic wave; ニュートラルネットワーク; 回折格子; 光コンピュータ; 超並列計算機; ホログラフィー; 波動; 位相; 画像処理

he project developed a novel approach for ultra parallel computation using interference of coherent waves as direct means of weighted snm computation for artificial neural networks. Coherent waves such as acoustic waves and lasers can interfere with each other and the amplitude of synthesized wave is determined by their phase differences. This phenomena can be utilized as means of weighted sum computation. Weighted sum is a basic operation of a neuron in the artificial neural networks which have been extensively studied for artificial intelligence purposes. The developed system uses only wave emitting and receiving devices as its components. The relative positions of these components determines the phase differences of the waves and, consequently, the ways of weighted sum. The kind of computation is coded as the positions of these components arranged on a two dimensional surface. In this fashion, the system does not require neither communication wires nor computation devices such as adder and multiplier. During the two years of this project, we proposed several ways of implementing this idea and examined the computational capabilities of the proposed systems under noisy and distorted wave conditions.

他的项目开发了一种新型的方法，使用一致波的干扰作为人工神经网络的加权SNM计算的直接手段。相干波（例如声波和激光器）可以互相干扰，而合成波的幅度取决于它们的相位差异。该现象可以用作加权总计计算的手段。加权总和是人工神经网络中神经元的基本操作，该神经元已被广泛研究以进行人工智能。开发的系统仅使用波浪发出和接收设备作为其组件。这些组分的相对位置决定了波的相位差，因此决定了加权总和的方式。该计算的种类被编码为这些组件在二维表面上排列的位置。以这种方式，该系统不需要通信电线，也不需要诸如加法器和乘数之类的计算设备。在该项目的两年中，我们提出了几种实施这一想法的方法，并在嘈杂和扭曲的波浪条件下检查了所提出的系统的计算能力。

项目成果

I. Kumazawa and Y. Kure: "Computation of Weighted Sum by Physical Wave Properties - Coding Problems by Unit Positions"In George D. Smith, Nigel C. Steele and Rudolf F. Albecht (eds.) Artificial Neural Nets and Genetic Algorithms. Springer Computer Science

I. Kumazawa 和 Y. Kure：“通过物理波特性计算加权和 - 按单位位置编码问题”，乔治 D. 史密斯、奈杰尔 C. 斯蒂尔和鲁道夫 F. 阿尔贝希特（编辑）人工神经网络和遗传算法。

I. Kumazawa: "A Cellular neural network framework for shape representation and matching"Proceedings of Third International Conference on Knowledge-based Intelligent Information Engineering Systems. IEEE. 178-181 (1999)

I. Kumazawa：“用于形状表示和匹配的细胞神经网络框架”第三届基于知识的智能信息工程系统国际会议论文集。

Itsuo Kumazawa (D. Smith(eds.)): "Artificial Neural Nets and Genetic Algorithms"Springer Computer Science. 634 (1998)

Ituo Kumazawa（D. Smith（编辑））：“人工神经网络和遗传算法”施普林格计算机科学。

石井真樹: "重み表現に線形の従属制約を導入した階層型ニューラルネットワークの写像能力"電子情報通信学会ニューロコンピューティング研究会. NC99-36. 57-62 (1999)

Maki Ishii：“引入权重表示的线性依赖约束的分层神经网络的映射能力”IEICE NC99-36 (1999)。

D.Smith(eds.): "Artificial Neural Nets and Genetic Algorithms"Springer Computer Science. (1998)

D.Smith（编）：“人工神经网络和遗传算法”施普林格计算机科学。