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

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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.