CSR--EHS: Dynamic Resource Management for Multimedia Applications on Embedded Systems

CSR--EHS：嵌入式系统多媒体应用的动态资源管理

• 批准号: 0509522
• 负责人: Mihaela van der Schaar
• 金额: $ 27万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509522&HistoricalAwards=false
• 关键词: CSR; EHS; Dynamic; Resource; Management

Existing approaches to implementing multimedia encoding and decoding algorithms on embedded systems have several drawbacks. First, multimedia algorithms are designed without explicitly taking resource constraints into account. Second, current embedded systems often assume "worst-case" resource utilization for the design and implementation of compression techniques, thereby neglecting the fact that multimedia algorithms require time-varying resources. Third, the algorithms and their implementation are not jointly optimized, resulting in sub-optimal performance. Finally, existing adaptive systems for multimedia are, at best, reactive, i.e., they adapt their resources to past data inputs, instead of modeling and predicting future resource requirements and planning accordingly. In this project, the abovementioned limitations are addressed to improve the efficiency of multimedia applications on embedded systems. Specifically, this research investigates dynamic resource planning techniques that can jointly optimize the complexity of the multimedia algorithms and the resource utilization, while meeting a given system constraint. This forms the basis for proactive resource management by using feed-forward control mechanisms like stochastic modeling, learning and forecasting. Finally, rate- and complexity-scalable compression schemes are necessary to enable optimized adaptation to available resources. Developing such an integrated application-implementation framework is of fundamental importance, since it not only leads to improved multimedia performance over existing embedded systems, but also provides valuable insights into the design of next generation multimedia compression algorithms that should be complexity scalable, as well as embedded systems designs that should be multimedia aware.The broader impact is in the area of educating next generation students and industry partners in resource-aware system design, which is critical to realize the vision of ubiquitous and autonomous computing systems of the future.

在嵌入式系统上实现多媒体编码和解码算法的现有方法有几个缺点。首先，多媒体算法的设计没有明确考虑资源限制。其次，当前的嵌入式系统经常假设“最坏情况”的资源利用来设计和实现压缩技术，从而忽略了多媒体算法需要时变资源的事实。第三，算法及其实现没有联合优化，导致性能次优。最后，现有的多媒体自适应系统充其量是反应性的，即它们将其资源适应过去的数据输入，而不是建模和预测未来的资源需求并相应地进行规划。在该项目中，解决了上述限制，以提高嵌入式系统上多媒体应用的效率。具体来说，本研究研究了动态资源规划技术，该技术可以联合优化多媒体算法的复杂性和资源利用率，同时满足给定的系统约束。这构成了通过使用随机建模、学习和预测等前馈控制机制进行主动资源管理的基础。最后，速率和复杂性可扩展的压缩方案对于实现对可用资源的优化适应是必要的。开发这样一个集成的应用程序实现框架至关重要，因为它不仅可以提高现有嵌入式系统的多媒体性能，而且还可以为下一代多媒体压缩算法的设计提供有价值的见解，该算法应该是复杂性可扩展的，以及嵌入式系统设计应具有多媒体意识。更广泛的影响在于教育下一代学生和行业合作伙伴进行资源感知系统设计，这对于实现未来无处不在的自主计算系统的愿景至关重要。