The invention of the Jacquard weaving machine led to the concept of a stored "program" and "mechanized" binary information

Wireless sensor networks operating on limited energy resources need to be power efficient to extend the system lifetime. This is especially challenging for video sensor networks due to the large volumes of data they need to process in short periods of time. Towards this end, this paper proposes two coordinated power management policies for video sensor networks. These policies are scalable as the system grows and flexible to video parameters and network characteristics. In addition to simulation results, our prototype demonstrates the feasibility of implementing these policies. Finally, the analytical framework we provide gives an upper bound for the achievable sleep fraction and insight into how adjusting select parameters will affect the performance of the power management policies.

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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.

Continuous advancements in semiconductor technology enable the design of complex systems-onchips (SoCs) composed of tens or hundreds of IP cores. At the same time, the applications that need to run on such platforms have become increasingly complex and have tight power and performance requirements. Achieving a satisfactory design quality under these circumstances is only possible when both computation and communication refinement are performed efficiently, in an automated and synergistic manner. Consequently, formal and disciplined system-level design methodologies are in great demand for future multiprocessor design. This article provides a broad overview of some fundamental research issues and state-of-the-art solutions concerning both computation and communication aspects of system-level design. The methodology we advocate consists of developing abstract application and platform models, followed by application mapping onto the target platform, and then optimizing the overall system via performance analysis. In addition, a communication refinement step is critical for optimizing the communication infrastructure in this multiprocessor setup. Finally, simulation and prototyping can be used for accurate performance evaluation purposes.