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Creators/Authors contains: "Sun, Yi"

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  6. A common problem practitioners face is to select rare events in a large dataset. Unfortunately, standard techniques ranging from pre-trained models to active learning do not leverage proximity structure present in many datasets and can lead to worse-than-random results. To address this, we propose EZMODE, an algorithm for iterative selection of rare events in large, unlabeled datasets. EZMODE leverages active learning to iteratively train classifiers, but chooses the easiest positive examples to label in contrast to standard uncertainty techniques. EZMODE also leverages proximity structure (e.g., temporal sampling) to find difficult positive examples. We show that EZMODE can outperform baselines by up to 130× on a novel, real-world, 9,000 GB video dataset.
    Free, publicly-accessible full text available December 14, 2022
  7. Excessive test power can cause multiple issues at manufacturing as well as during field test. To reduce both shift and capture power during test, we propose a DFT-based approach where we split the scan chains into segments and use extra control bits inserted between the segments to determine whether a particular segment will capture. A significant advantage of this approach is that a standard ATPG tool is capable of automatically generating the appropriate values for the control bits in the test patterns. This is true not only for stuck-at fault test sets, but for Launch-off-Capture (LOC) transition tests as well. It eliminates the need for expensive post processing or modification of the ATPG tool. Up to 37% power reduction can be achieved for a stuck-at test set while up to 35% reduction can be achieved for a transition test set for the circuits studied.
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