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Iterative optimization is a popular compiler optimization approach that has been studied extensively over the past decade. In this article, we deconstruct iterative optimization by evaluating whether it works across datasets and by analyzing why it works. Up to now, most iterative optimization studies are based on a premise which was never truly evaluated:(More)
While iterative optimization has become a popular compiler optimization approach, it is based on a premise which has never been truly evaluated: that it is possible to learn the best compiler optimizations across data sets. Up to now, most iterative optimization studies find the best optimizations through repeated runs on the same data set. Only a handful(More)
Because of tight power and energy constraints, industry is progressively shifting toward <i>heterogeneous</i> system-on-chip (SoC) architectures composed of a mix of general-purpose cores along with a number of accelerators. However, such SoC architectures can be very challenging to efficiently program for the vast majority of programmers, due to numerous(More)
Heterogeneous multi-cores, a mix of cores and accelerators, are becoming prevalent. These accelerators are designed for both speed and energy improvements, and thus, they increasingly come with a large number of load/store ports for achieving a high degree of parallelism. However, beyond GPG-PUs, accelerators such as ASICs and CGRAs are increasingly capable(More)
We propose a novel spatiotemporal graphical model for unsupervised video object segmentation. The core of our model is a layered-CRF (conditional random field) that contains two layers, i.e., pixel layer and supervoxel layer. First, the heat diffusion based segmentation and salient region detection is integrated to obtain the segmentation results of the(More)
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