The primary design goal of many embedded systems for multimedia applications is usually meeting the performance requirement at a minimum cost. In this thesis, we proposed two different ILP based approaches for hardware/software (HW/SW) partitioning and pipelined scheduling of embedded systems for multimedia applications. One ILP approach solves the HW/SW partitioning and pipelined scheduling problem simultaneously. Another ILP approach separates the HW/SW partitioning and pipelined scheduling problem into two phases. The first phase is focusing on the HW/SW partitioning and mapping problem. Second phase is used to solve the pipelined scheduling problem. The two ILP approaches not only partition and map each computation task of a particular multimedia application onto a component of the heterogeneous multiprocessor architecture, but also schedules and pipelines the execution of these computation tasks while considering communication time. For the first ILP model, the objective is to minimize the total component cost and the number of pipeline stages subject to the throughput constraint. In the second ILP approach, the objective of the first phase and second phase is to minimize the total component cost and the number of pipeline stages subject to the throughput constraint, respectively. Finally, experiments on three real multimedia applications (JPEG Encoder, MP3 Decoder, Wavelet Video Encoder) are used to demonstrate the effectiveness of the proposed approaches.

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Robert F. Dell, William J. Tarantino, Eva D. Regnier. Includes bibliographical references (p. 45-47). Also available online.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 71-75).

As device geometries shrink, variability of process parameters becomes pronounced, resulting in a significant impact on the power and timing performance of integrated circuits. Deterministic optimization algorithms for power and area lack capabilities for handling uncertainty, and may lead to over-conservative solutions. As a result, there is an increasing need for statistical algorithms that can take into account the probabilistic nature of process parameters. Statistical optimization techniques however suffer from the limitation of high computational complexity. The objective of this work is to develop efficient algorithms for optimization of area and power under process variability while guaranteeing high yield. The first half of the dissertation focuses on two design-time techniques: (i) a gate sizing approach for area minimization under timing variability; (ii) an algorithm for total power minimization considering variability in timing and power. Design-time methods impose an overhead on each instance of the fabricated chip since they lack the ability to react to the actual conditions on the chip. In the second half of the dissertation we develop joint design-time and post-silicon co-optimization techniques which are superior to design-time only optimization methods. Specifically, we develop (i) a methodology for optimization of leakage power using design-time sizing and post silicon tuning using adaptive body bias; (ii) an optimization technique to minimize the total power of a buffer chain while considering the finite nature of adaptability afforded. The developed algorithms effectively improve the overconservatism of the corner-based deterministic algorithms and permit us to target a specified yield level accurately. As the magnitude of variability increases, it is expected that statistical algorithms will become increasingly important in future technology generations. / text

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In a standard tennis match each player has an unlimited opportunity to challenge an umpire’s call, but if three incorrect challenges are made in a set he is not allowed to challenge anymore in that set. If the set goes into a tie break the limit on incorrect challenges increases by one. These limited incorrect challenges are not carried over from one set to another. So this is kind of a limited resource available to the player and if he knows how to use this resource in a best possible way, there is a scope for increasing his overall chances of winning a match. With the motive of gaining insight on when to challenge a call, we have modeled a single game in a tennis match as a Markov decision process. We have also studied the impact of variables like player’s probability of winning a point, the player’s perception of the challengability of a call and proportion of challengable calls on the decision making process. / text

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