As the process technology advances, reducing the leakage power as much as possible is one of the utmost challenging tasks in chip implementation. Utilizing cells with multi-VT (threshold voltage) is known to be a very effective method for optimizing leakage power under timing constraints. However, for the sequential cells on the timing critical paths in circuits, there is no easy way for the multi-VT method to reduce the leakage power unless timing is not sacrificed. To overcome this barrier, we introduce a set of new standard cells called hybrid-VT flipflop cells, each of which is implemented with two different VT types, one implanted onto its master latch while the other onto its slave latch, by which the setup time and clock-to-Q delay can be controlled individually and independently. We confirm that applying our power recovery method utilizing hybrid-VT flipflop cells to the benchmark circuits, which have already been optimized by the conventional multi-VT cells, is able to further reduce the leakage power by 8.97% with no timing degradation.

Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularization is essential for point clouds since lack of generality is more likely to occur in point cloud due to small datasets. This paper proposes a Rigid Subset Mix (RSMix)1, a novel data augmentation method for point clouds that generates a virtual mixed sample by replacing part of the sample with shape-preserved subsets from another sample. RSMix preserves structural information of the point cloud sample by extracting subsets from each sample without deformation using a neighboring function. The neighboring function was carefully designed considering unique properties of point cloud, unordered structure and non-grid. Experiments verified that RSMix successfully regularized the deep neural networks with remarkable improvement for shape classification. We also analyzed various combinations of data augmentations including RSMix with single and multi-view evaluations, based on abundant ablation studies.

Troubleshooting imperative programs at runtime is very challenging because the final optimized plan is quite different from the original design time model. In this demonstration, we present ProcAnalyzer, an expressive and intuitive tool for troubleshooting issues related to performance, code quality, and security. We propose end-to-end graph (E2EGraph) that provides a holistic view of design time, compile time, and runtime behavior so that end users and engine developers easily find the correlations between design time and runtime. ProcAnalyzer provides suggestions and visualization to find problematic statements through the E2EGraph.