Successful proliferation of multimedia-enabled devices and advances in very large-scale integration (VLSI) technology has spawned new research efforts in migrating video processing applications onto ever smaller and more inexpensive devices. This article focuses on the technical challenges associated with that migration. Due to limitations in size, battery lifetime, and, ultimately, cost, mapping complex video applications onto resource-constrained systems is a very challenging proposition. To this end, we first consider a technique, region-of-interest (ROI) processing, of defining a window within a video frame and only operating on the data inside that window, ignoring the rest of the frame. By using this lossy technique, the processing requirements can be reduced by roughly 80% while the error introduced in the quality of the results is roughly 10%. The other technique is adaptive data partitioning (ADP) combined with a content-based power management algorithm. By distributing video processing among multiple processors and shutting them down when they are not needed, the energy consumed per processor can be reduced by 60% without sacrificing the performance of the underlying video-based application. Taken together, these novel techniques enable ambient multimedia systems and maintain the needed overall efficiency in video processing.

The objective of this article is to introduce the use of Stochastic Automata Networks (SANs) as an effective formalism for application-architecture modeling in system-level average-case analysis for platform-based design. By platform, we mean a family of heterogeneous architectures that satisfy a set of architectural constraints imposed to allow re-use of hardware and software components. More precisely, we show how SANs can be used early in the design cycle to identify the best performance/power trade-offs among several application-architecture combinations. Having this information available not only helps avoid lengthy simulations for predicting power and performance figures, but also enables efficient mapping of different applications onto a chosen platform. We illustrate the benefits of our methodology by using the “Picture-in-Picture” video decoder as a driver application.

In the near future, Ambient Intelligence (AmI) will become part of everyday life. Combining feature-rich multimedia with AmI (dubbed Ambient Multimedia for short) has the potential of changing the way we perceive and interact with our environment. One major difficulty, however, in designing Ambient Multimedia Systems (AMS) comes from the strong constraints imposed on system resources by the AmI application requirements. In this paper, we propose a method for mapping multimedia applications on systems with very limited resources (i.e. memory, computing capability and battery lifetime) by combining adaptive data partitioning with dynamic power management. The potential of the approach is illustrated through a case study of an object tracking application running on a resource-constrained platform.

In this paper, we propose a method for mapping multimedia applications on systems with very limited resources (i.e. memory, computing capability and battery lifetime) by combining adaptive data partitioning with content-based dynamic power management. The potential of the approach is illustrated through a case study of an object tracking application running on a resource constrained system which can be embedded in the environment (e.g. offices, home or conference rooms) to offer significantly more opportunities for ubiquitous information, seamless communication, enhanced security, etc. compared to today's portable or stationary devices. Besides power and performance trade-offs, we also explore the scaling effects on data partitioning and provide insights for possible optimization when designing such systems

Ambient intelligence (AmI) is the inconspicuous presence of computing into every facet of our lives. AmI systems of the future will contain devices with highly limited resources in terms of processing power, memory, and battery lifetime. Contrary to this are the memory, power, and cycle-hungry video processing applications which are required to provide the level of utility demanded by users. The paper introduces the idea of processing a portion of a video frame with the intent of achieving high levels of video processing performance while reducing considerably the hardware requirements for that processing. The techniques we propose operate only on a portion of the video frame and optionally adjust that portion's size dynamically to match the video content reasonably well. Our results show that this technique can save roughly 75% in both memory and processing cycle requirements and up to 87% in energy consumption, while inducing less than 10% error in XY processing of the video frame