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Found 7 papers, 0 papers with code
Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning
Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing.
Deep Feature Selection Using a Novel Complementary Feature Mask
However, most existing feature selection approaches, especially deep-learning-based, often focus on the features with great importance scores only but neglect those with less importance scores during training as well as the order of important candidate features.
A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning
In this sense, an more efficient tuning requires identifying the most critical tuning knobs and process parameters in terms of a given figure-of-merit for a Device Under Test (DUT).
Conditional Variable Selection for Intelligent Test
Intelligent test requires efficient and effective analysis of high-dimensional data in a large scale.
Self-Learning Tuning for Post-Silicon Validation
Increasing complexity of modern chips makes design validation more difficult.
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\
In PSV, the task is to approximate the underlying function of the data with multiple learning algorithms, each trained on a device-specific subset, instead of improving the performance of arbitrary classifiers on the entire data set.
Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization
Feature selection is generally used as one of the most important preprocessing techniques in machine learning, as it helps to reduce the dimensionality of data and assists researchers and practitioners in understanding data.
