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Two Dimensional Parallel Signaling an Detection Techniques With Applications To Volume Optical Memories

Two Dimensional Parallel Signaling an Detection Techniques With Applications To Volume Optical Memories
二维并行信号传输和检测技术及其在体积光学存储器中的应用
批准号:
9616663
负责人:
Keith Chugg
金额:
$18.68万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1996
资助国家:
美国
项目状态:
已结题
起止时间:
1996-09-01 至 2000-08-31

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中文摘要
翻译
并行二维存储器(即,存储和检索数字数据的2D页面(阵列)的存储器)表示根本不同的存储器架构。这种2D并行架构具有容量和速度性能的潜力,其数量级超出传统1D存储架构的预期物理限制。 体积光学存储器是在三维光学介质内存储数据页的系统。 由于数据的2D存储/检索,这些系统具有大存储容量(10^12位/立方厘米)、经由无质量扫描的高速随机存取(10 ns)和非常大的数据传输速率(10^10位/秒)的潜在组合。直接应用传统的通信理论技术的2D并行存储器是复杂的真正的2D性质的数据格式。此外,卷光学存储器实现通常导致具有非常复杂的噪声和干扰的存储通道。对操作环境有贡献的因素包括(I)作为光电转换的结果的相干光信号的强度变换和随后的空间平均,(ii)非高斯噪声源,诸如散斑和散斑,以及(iii)复杂的数据相关干扰源,诸如给定页面内的页面间串扰和空间变化符号间干扰。 该研究计划涉及信令,均衡和决策方案,因为它们适用于并行2D数据通道,并特别考虑到相干(全息)体积光存储系统。该研究项目的一个目标是为2D数字通信系统中的信令和检测奠定理论基础。另一个目标是应用这一理论开发强大的,可实现的信令和检测技术的体积光存储器系统。 实现这些目标的计划是基于开发相对简单的体积光存储系统模型,用于开发和分析特定的信令和检测算法。性能界限的最佳检测策略,以及有效的算法实现,将寻求在数据检测阶段的研究。 简单的,次优的数据检测器,利用2D性质的数据格式也将开发和其性能的特点相对于最佳的检测规则。信号设计组件将关注与2D非对称信道使用的纠错编码,用于对抗页面内和页面之间的数据相关干扰的预编码技术,使用简单的光学掩模的像素轮廓优化,以及非高斯信道的新型信令策略。第三个组成部分涉及创建一个复杂的模拟引擎,用于评估和改进新的信号和检测方法。该仿真引擎将捕获卷光存储器系统的详细物理特性,并将有助于测量这些新的2D方法在现实环境中的性能和鲁棒性。
英文摘要
A parallel two-dimensional memory (i.e., one that stores and retrieves 2D pages (arrays) of digital data) represents a fundamentally different memory architecture. This 2D parallel architecture has the potential for capacity and speed performance which is orders of magnitude beyond the expected physical limits of traditional lD storage architectures. A volume optical memory is a system which stores data pages within a three dimensional optical medium. These systems have the potential combination of large storage capacity ( 10^l2 bits/cm^3), high speed random access via massless scanning (10 ns), and very large data transfer rates ( 10^10 bits/sec) owing to the 2D storage/retrieval of data.. Direct application of conventional communication theoretic techniques to 2D parallel memories is complicated by the truly 2D nature of the data format. In addition, a volume optical memory implementation typically results in a storage channel with very complex noise and interference. Factors contributing to the operating environment include (I) the intensity transformation and subsequent spatial averaging of the coherent optical signal as a result of optoelectronic conversion, (ii) non Gaussian noise sources such as shot and speckle, and (iii) complex data dependent interference sources such as inter-page crosstalk and space variant intersymbol interference within a given page. This research program is concerned with signaling, equalization, and decision schemes as they apply to parallel 2D data channels, with specific consideration given to coherent (holographic) volume optical memory systems. One objective of this research program is to develop the theoretical foundation for signaling and detection in 2D digital communication systems. Another objective is the application of this theory to develop robust, implementable signaling and detection techniques for volume optical memory systems. The plan for achieving these objectives is based on the development of relatively simp le models of the volume optical memory system to be used for development and analysis of specific signaling and detection algorithms. Performance bounds for the optimal detection strategies, as well as efficient algorithms for their realization, will be sought in the data detection phase of the research. Simple, suboptimal data detectors which exploit the 2D nature of the data format will also be developed and their performance characterized relative to that of the optimal detection rule. The signal design component will be concerned with error correction coding for use with 2D asymmetric channels, precoding techniques for combating data-dependent interference within and between pages, pixel profile optimization using simple optical masks, and novel signaling strategies for non-Gaussian channels. The third component involves the creation of a sophisticated simulation engine for the evaluation and refinement of new signaling and detection methods. This simulation engine will capture the detailed physical characteristics of volume optical memory systems and will facilitate measuring the performance and robustness of these novel 2D approaches in a real-world environment.
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会议论文
SHF: Medium: Training Sparse Neural Networks with Co-Designed Hardware Accelerators: Enabling Model Optimization and Scientific Exploration
  • 批准号:
    1763747
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $119.98万
  • 财政年份:
    2018
  • 负责人:
    Keith Chugg
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis