CIF: Medium: Analog Architectures for Optimization in Signal Processing

CIF：中：用于优化信号处理的模拟架构

• 批准号: 0905346
• 负责人: Christopher Rozell
• 金额: $ 90.66万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905346&HistoricalAwards=false
• 关键词: CIF; Medium; Analog; Architectures; Optimization

Abstract:Modern techniques giving the best performance for acquiring and processing signals/images rely on repeatedly solving mathematical optimization problems which can be computationally expensive. This research involves advancing, by orders of magnitude, the state of the art for solving an important class of these problems. Rather than developing algorithms tailored to current digital computational platform, the investigators depart completely from this current line of research to study analog architectures for solving these problems. These analog architectures, when fully developed, have the potential for dramatic gains in speed and power efficiency over their digital counterparts. This research project is inherently multidisciplinary, as it combines recent advances in computational neuroscience, signal processing, and reconfigurable VLSI architectures. Among other applications, these systems enable reductions in the time needed to acquire a magnetic resonance image (MRI).This project focuses primarily on solving optimization programs combining a mean-squared error data fidelity term with a sparsity inducing cost function (e.g., the L1 norm) via an analog dynamical system architecture. Specifically , the project contains two intertwined threads: circuit implementation and mathematical analysis. The goal of the circuit implementation thread is to produce a analog circuit which solves significant optimization programs (e.g., tens of thousands of variables) substantially faster than state-of-the-art digital solutions. The investigators leverage recent advances in reconfigurable analog architectures to achieve efficient designs at this large scale. The analysis thread includes deriving bounds on the circuit convergence time and generalizing the architecture to include other relevant signal processing problems. The research also involves applying this analog architecture as a nonlinear "filter" which continuously reacts to changes in the input.

翻译后摘要：现代技术的最佳性能的采集和处理信号/图像依赖于反复求解数学优化问题，这可能是计算昂贵的。 这项研究涉及推进，由数量级，解决这些问题的一个重要类的最先进的。 研究人员没有开发针对当前数字计算平台的算法，而是完全脱离当前的研究路线，研究解决这些问题的模拟架构。这些模拟架构在充分开发后，与数字架构相比，有可能在速度和功率效率方面大幅提高。 这个研究项目本质上是多学科的，因为它结合了计算神经科学，信号处理和可重构VLSI架构的最新进展。 在其他应用中，这些系统能够减少获取磁共振图像（MRI）所需的时间。该项目主要集中在解决将均方误差数据保真度项与稀疏性诱导成本函数（例如，L1范数）。 具体来说，该项目包含两个相互交织的线程：电路实现和数学分析。电路实现线程的目标是产生解决重要优化程序（例如，成千上万的变量），大大快于最先进的数字解决方案。 研究人员利用可重构模拟架构的最新进展来实现这种大规模的高效设计。 分析线程包括推导电路收敛时间的界限，并将架构推广到包括其他相关的信号处理问题。 该研究还涉及将这种模拟架构应用为非线性“滤波器”，该非线性“滤波器”不断对输入的变化做出反应。