Sharif Digital Repository

Within-die process variation is increasing in nanometer-scale process technologies. We observe that the same SRAM cell leaks differently under within-die process variations when storing 0 compared to 1; this difference can be up to 3 orders of magnitude at 60mV variation of threshold voltage (V th). Thus, leakage can be reduced if most often the values that dissipate less leakage are stored in the cache SRAM cells. We take advantage of this fact to reduce instruction-cache leakage by presenting three binary-optimization and software-level techniques: we (i) reorder instructions within basic-blocks so that their bits better match the less-leaky state of their corresponding cache cells, (ii)... 

While performance and power consumption of processors present a classic trade-off in designing embedded hardware, software can be optimized in favor of both performance and energy. We evaluate the impact of optimizations at different stages of designing embedded software. We show that algorithm choice and compiler optimizations aimed at improving performance can also reduce energy consumption of an embedded processor. We also propose energy-aware compilation guidelines which can further reduce energy consumption without performance penalties. Our experimental results show that up to 85% energy reduction and 89% performance improvement can be achieved by these techniques  

Edit distance is one of the most fundamental problems in computer science. Tree edit distance is a natural generalization of edit distance to ordered rooted trees. Such a generalization extends the applications of edit distance to areas such as computational biology, structured data analysis (e.g., XML), image analysis, and compiler optimization. Perhaps the most notable application of tree edit distance is in the analysis of RNA molecules in computational biology where the secondary structure of RNA is typically represented as a rooted tree. The best-known solution for tree edit distance runs in cubic time. Recently, Bringmann et al. show that an O(n2.99) algorithm for weighted tree edit...