In this paper, we study the interdependency between leakage energy and chip temperature in real-time systems. We observe that the temperature variation on chip has a large impact on the system's leakage energy. By incorporating the temperature information, we propose an online temperatureaware leakage minimization algorithm for real-time systems. The basic idea is to run tasks when the system is cool and the workload is high, and put the system into sleep when it is hot and the workload is light. This online algorithm has low run-time complexity and improve the leakage energy saving by 34% on average in both real life and artificial benchmarks over traditional DVS approaches. Finally, our algorithm can be combined with existing dynamic voltage scaling methods to further improve the total energy efficiency.

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Abstract-Recent research has shown that forwarding speculative data to other processors before it is requested can improve the performance of multiprocessor systems. The most recent work in speculative data forwarding places all of the processors on a single bus, allowing the data to be forwarded to all of the processors at the same cost as any subset of the processors. Modern multiprocessors however often employ more complex switching networks in which broadcast is expensive. Accurately predicting the consumers of data can be challenging, especially in the case of programs with many shared data structures.Past consumer predictors rely on simple prediction mechanisms, a single table lookup followed by a static mapping of the table values onto a prediction. We make two main contributions in this paper. First, we show how to reduce the design space of consumer predictors to a set of interesting predictors, and how previous consumer predictors can be tuned to expand the range of available performance. Second, we propose a perceptron consumer predictor that dynamically adapts its reaction to the system behavior, and uses more history information than previous consumer predictors. This predictor outperforms the previous predictors by 21% while using only 1KByte more storage than previous predictors.

Offset assignment has been studied as a highly effective approach to code optimization in modern digital signal processors (DSPs). In this" paper, we propose two evolutionary algorithms to solve the general of~et assignment problem with k address registers and an arbitrary auto-modi~ range. These algorithms differ from previous algorithms by having the capabilio~ of visiting the entire search apace. We implement and analyze a variety of existing general offset assignment algorithms and test them on a set of standard benchmarks. The algorithms we propose can achieve a performance improvement of up to 31% over the best existing algorithm. We also achieve an average of 14% improvement over the union of recently proposed algorithms. 2°