CSR: Small: Dynamically Reconfigurable Architectures for Time-Varying Image Constraints (DRASTIC) Based on Local Modeling and User Constraint Prediction

CSR：小型：基于局部建模和用户约束预测的时变图像约束 (DRASTIC) 动态可重构架构

• 批准号: 1422031
• 负责人: Marios Pattichis
• 金额: $ 45.99万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422031&HistoricalAwards=false
• 关键词: CSR; Small; Dynamically; Reconfigurable; Architectures

The use of digital video in embedded and communications systems has risen dramatically in recent years. User needs and interests in digital video vary based on video content and available computing resources such as battery life and communications bandwidth. Thus, there is a strong need to develop methods that can dynamically reconfigure hardware and software resources in real-time to respond to changing video content, user needs, or available computing resources. The proposed research will develop methods that will provide run-time management of hardware and software resources for video processing and communications that are jointly optimal in terms of energy, bandwidth, and throughput.Digital video processing and communication often consumes the majority of computing resources and bandwidth. With the emergence of the High-Efficiency Video Coding (HEVC) standard, there is a focus on the development of parallel architecture solutions that can effectively provide real-time coding of high-resolution video while reducing the bandwidth requirements up to 50% from the previous coding standard (H.264/AVC). However, the HEVC's focus on rate-distortion optimization does not consider how computing architectures should adapt to time-varying energy constraints. The proposed research will be focused on the development of Dynamically Reconfigurable Architecture Systems for satisfying Time-varying Image processing Constraints (DRASTIC) that optimize computing resources to satisfy time-varying constraints on energy, bandwidth, and image quality for HEVC and video analysis based on 2D/3D filterbanks. The research is transformative in two different ways: (i) it supports the automatic generation of real-time varying constraints based on video content and available energy while eliminating the need for user inputs, and (ii) it uses a local model to significantly reduce the requirements for estimating large Pareto fronts over a large space of videos. These transformative approaches can significantly expand the applicability of the proposed system and methods.

近年来，数字视频在嵌入式和通信系统中的使用急剧上升。用户对数字视频的需求和兴趣因视频内容和可用的计算资源(如电池寿命和通信带宽)而异。因此，强烈需要开发能够实时动态地重新配置硬件和软件资源以响应不断变化的视频内容、用户需求或可用计算资源的方法。拟议的研究将开发方法，为视频处理和通信提供硬件和软件资源的运行时管理，在能量、带宽和吞吐量方面共同优化。数字视频处理和通信通常消耗大部分计算资源和带宽。随着高效视频编码(HEVC)标准的出现，人们关注于开发并行体系结构解决方案，该解决方案可以有效地提供高分辨率视频的实时编码，同时将带宽需求从先前的编码标准(H.264/AVC)降低高达50%。然而，HEVC对率失真优化的关注并没有考虑计算体系结构应该如何适应时变的能量约束。这项研究将集中于开发动态可重构的体系结构系统，以满足时变图像处理约束(DRASTIC)，该系统优化计算资源以满足HEVC和基于2D/3D滤波器组的视频分析的时变能量、带宽和图像质量约束。这项研究在两个不同的方面具有变革性：(I)它支持基于视频内容和可用能量自动生成实时变化的约束，同时消除了对用户输入的需要；(Ii)它使用本地模型显著降低了在大视频空间中估计大的帕累托前沿的要求。这些变革性的方法可以极大地扩展所提出的系统和方法的适用性。