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

近年来，数字视频在嵌入式和通信系统中的应用急剧增加。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，迫切需要开发能够实时动态地重新配置硬件和软件资源的方法，以响应不断变化的视频内容、用户需求或可用的计算资源。提出的研究将开发方法，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往往消耗大部分的计算资源和带宽。随着高效视频编码（HEVC）标准的出现，人们开始关注并行架构解决方案的开发，这些解决方案可以有效地提供高分辨率视频的实时编码，同时将带宽要求从以前的编码标准（H.264/AVC）降低到50%。然而，HEVC对率失真优化的关注并没有考虑计算架构应该如何适应时变的能量约束。提出的研究将侧重于开发动态可重构架构系统，以满足时变图像处理约束（DRASTIC），优化计算资源，以满足基于2D/3D滤波器组的HEVC和视频分析的能量，带宽和图像质量的时变约束。该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的实时变化约束的自动生成，同时消除了对用户输入的需要；（ii）它使用局部模型显著减少了在大视频空间上估计大型帕累托前沿的需求。这些变革性的方法可以显著地扩展所提出的系统和方法的适用性。

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.