Abstract. Self-timed scheduling is an attractive implementation style for multiprocessor DSP systems due to its ability to exploit predictability in application behavior, its avoidance of over-constrained synchronization, and its simplified clocking requirements. However, analysis and optimization of selftimed systems under real-time constraints is challenging due to the complex, irregular dynamics of selftimed operation. In this paper, we review a number of high-level intermediate representations for compiling dataflow programs onto self-timed DSP platforms, including representations for modeling the placement of interprocessor communication (IPC) operations; separating synchronization from data transfer during IPC; modeling and optimizing linear orderings of communication operations; performing accurate design space exploration under communication resource contention; and exploring alternative processor assignments during the synthesis process. We review the structure of these representations, and discuss efficient techniques that operate on them to streamline scheduling, communication synthesis, and power management of multiprocessor DSP implementations.
Abstract-Application-specific, parameterized local search algorithms (PLSAs), in which optimization accuracy can be traded off with run time, arise naturally in many optimization contexts. We introduce a novel approach, called simulated heating, for systematically integrating parameterized local search into evolutionary algorithms (EAs). Using the framework of simulated heating, we investigate both static and dynamic strategies for systematically managing the tradeoff between PLSA accuracy and optimization effort. Our goal is to achieve maximum solution quality within a fixed optimization time budget. We show that the simulated heating technique better utilizes the given optimization time resources than standard hybrid methods that employ fixed parameters, and that the technique is less sensitive to these parameter settings. We apply this framework to three different optimization problems, compare our results to the standard hybrid methods, and show quantitatively that careful management of this tradeoff is necessary to achieve the full potential of an EA/PLSA combination.Index Terms-Evolutionary algorithm (EA), hybrid global/local search.
We have grown ZnCdSe/ZnCdMgSe quantum well (QW) structures nearly lattice matched to InP substrates. Emission energies from 2.307 to 2.960 eV were measured by low-temperature photoluminescence at 10 K for samples with QW thicknesses between 5 and 80 Å. By using exactly lattice-matched QWs, the lower limit of the energy range can be lowered to about 2.2 eV (at 10 K). We propose that these structures could be used in entirely lattice-matched semiconductor lasers operating at room temperature in the blue, green, and yellow regions. Because of the absence of strain, these materials are expected to be less prone to degradation than the current blue-green lasers grown on GaAs.
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