Hyperspace Processing for Large EDA Dataset Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electronic design automation (EDA) processes face challenges in efficiently processing large datasets due to O(n^2) complexity, leading to impractical computation times and resource requirements, particularly in tasks like optical proximity correction (OPC) model calibrations and hotspot predictions, which are inaccurate and time-consuming using current techniques.
Innovation Solution
The use of hyperspace generation and hyperspace-based processing, which involves transforming a feature space into a set of hyperboxes to efficiently process large datasets, reducing complexity to O(n) and improving computational efficiency and accuracy in EDA tasks such as OPC model calibrations and hotspot predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification techniques (e.g., K-means clustering, mahalanobis cluster computations) are used to process large datasets, then classification capability is provided, but computational complexity becomes O(n^2) leading to inordinate processing time
Solution Approach 1:
The patent segments the feature space into multiple clusters using a tree-based structure where data points are recursively partitioned into sub-regions. This segmentation allows classification to be performed in O(n) time by traversing the tree structure rather than comparing all data points pairwise, thus resolving the contradiction between classification accuracy and processing time.
Solution Approach 2:
The patent introduces a hierarchical tree structure dimension to organize data points in feature space. By adding this structural dimension, the system achieves efficient O(n) classification while maintaining the accuracy benefits of sophisticated clustering algorithms, as the tree structure enables rapid navigation to relevant data regions.
2Measurement precision
If sophisticated classification techniques are applied to large EDA datasets, then processing accuracy may be improved, but computational resources required become impractically large
Solution Approach 1:
The patent divides the computational task into manageable segments by organizing data into a tree structure with root and child nodes. Each node processes only its local subset of data, reducing the overall computational burden while maintaining global classification accuracy through hierarchical aggregation of results.
Solution Approach 2:
The patent performs partial classification actions by traversing only the necessary paths in the tree structure rather than processing all data points uniformly. This selective processing reduces computational resource requirements while achieving sufficient accuracy for the application.
3Quantity of substance
If down sampling is performed on large datasets using traditional methods, then data reduction is achieved, but outlier data points necessary for accurate processing may be lost
Solution Approach 1:
The patent applies different sampling strategies to different regions of the feature space based on local data characteristics. By identifying outlier regions through the tree structure and applying targeted sampling to those specific areas, the system reduces dataset size while preserving critical outlier data points that would be lost in uniform random sampling.
Solution Approach 2:
The patent performs preliminary identification of outlier regions and important data points before down sampling. By pre-processing the data to mark significant regions in the tree structure, the system ensures that critical outliers are preserved during subsequent data reduction, maintaining processing reliability.
Data Source
AI summary
A computing system may include a hyperspace generation engine and a hyperspace processing engine. The hyperspace generation engine may be configured to access a feature vector set, and feature vectors in the feature vector set may represent values for multiple parameters of data points in a dataset. The hyperspace generation engine may further be configured to perform a principal component analysis on the feature vector set and quantize the principal component space into a hyperspace comprised of hyperboxes. The hyperspace processing engine may be configured to process the dataset according to a mapping of the feature vector set into the hyperboxes of the hyperspace.