SHF: Small: Stochastic Computing Techniques for Real-Time Image-Processing Applications

SHF：小型：实时图像处理应用的随机计算技术

• 批准号: 1318091
• 负责人: John Hayes
• 金额: $ 45万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318091&HistoricalAwards=false
• 关键词: SHF; Small; Stochastic; Computing; Techniques

Many beneficial applications of computers cannot now be realized because they demand excessively high performance and/or low power. An example of interest is real-time image processing to restore sight to the visually impaired. This project explores an unconventional and little-understood technology called stochastic computing (SC) which is very well-suited to such applications. Stochastic Computing processes numbers in the form of bit-streams that resemble neural signals and are interpreted as probabilities. It can implement complex arithmetic operations by small logic circuits. However, high accuracy may require long bit-streams that can be difficult to interface with conventional binary logic. The project aims to develop a comprehensive theory of SC leading to practical methods for designing and applying stochastic circuits. It will study the foundations of SC, especially speed, accuracy and hardware-cost trade-offs, using various novel methods. Stochastic Computing will be applied to a broad set of image-processing tasks, ranging from retinal implants for the blind, to on-the-fly feature extraction. Prototype designs will be constructed and evaluated using software simulation and hardware emulation via field-programmable gate arrays. The project's goal is a full theoretical and practical understanding of stochastic computing in the context of emerging integrated circuit technologies and applications. Its results should interest research engineers and scientists in academe, as well as in the microelectronics, computer, and bioengineering industries. They should also be of direct practical value to designers and manufacturers in such application areas as image-processing chips, implantable medical devices, and video surveillance systems. The project's outputs will be distributed primarily via peer-reviewed journal and conference papers. A key goal is to support the training of graduate students in computer science and engineering, who will participate directly in the research as part of their M.S. and Ph.D. programs at the University of Michigan. A few undergraduates will also be invited to join the project as interns to encourage them to pursue research-related careers. A special effort will be made to involve women and minority students.

计算机的许多有益应用现在不能实现，因为它们要求过高的性能和/或低功率。一个令人感兴趣的例子是实时图像处理，以恢复视力受损。该项目探索了一种非常规且鲜为人知的技术，称为随机计算（SC），非常适合此类应用。随机计算以比特流的形式处理数字，这些比特流类似于神经信号，并被解释为概率。它可以用小型逻辑电路实现复杂的算术运算。然而，高精度可能需要长的比特流，这可能难以与传统的二进制逻辑接口。 该项目旨在发展一个全面的理论SC导致设计和应用随机电路的实用方法。它将研究SC的基础，特别是速度，精度和硬件成本的权衡，使用各种新的方法。 随机计算将被应用于一系列广泛的图像处理任务，从盲人的视网膜植入到实时特征提取。原型设计将通过现场可编程门阵列使用软件模拟和硬件仿真进行构建和评估。该项目的目标是在新兴集成电路技术和应用的背景下对随机计算进行全面的理论和实践理解。其结果应该感兴趣的研究工程师和科学家在科隆，以及在微电子，计算机和生物工程行业。它们还应该对图像处理芯片、植入式医疗设备和视频监控系统等应用领域的设计者和制造商具有直接的实用价值。该项目的产出将主要通过同行审查的期刊和会议文件分发。一个关键目标是支持计算机科学与工程研究生的培训，他们将直接参与研究，作为他们的硕士学位的一部分。和博士密歇根大学的课程。此外，亦会邀请部分本科生以实习生身份参与计划，以鼓励他们从事与研究有关的工作。将特别努力让妇女和少数民族学生参与。