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

现有的在嵌入式系统上实现多媒体编码和解码算法的方法存在一些缺陷。首先，多媒体算法的设计没有明确考虑资源约束。其次，当前的嵌入式系统通常在设计和实现压缩技术时假设“最坏情况”的资源利用率，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被动的，也就是说，它们根据过去的数据输入调整其资源，而不是建模和预测未来的资源需求并相应地进行规划。在这个项目中，解决了上述的限制，以提高多媒体应用在嵌入式系统上的效率。具体而言，本研究探讨了动态资源规划技术，该技术可以在满足给定系统约束的情况下，共同优化多媒体算法的复杂性和资源利用率。通过使用前馈控制机制（如随机建模、学习和预测），这形成了主动资源管理的基础。最后，速率和复杂度可伸缩的压缩方案是必要的，以实现对可用资源的优化适应。开发这样一个集成的应用程序实现框架是至关重要的，因为它不仅可以改善现有嵌入式系统的多媒体性能，而且还为下一代多媒体压缩算法的设计提供了有价值的见解，这些算法应该是复杂可扩展的，嵌入式系统设计应该是多媒体感知的。更广泛的影响是在资源感知系统设计方面教育下一代学生和行业合作伙伴，这对于实现未来无处不在和自主计算系统的愿景至关重要。

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.