iCAM Feature Search for Sparse Coding
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Solution Overview
Problem
Existing methods for nearest neighbor searching in high-dimensional feature spaces are inefficient and power-intensive, particularly for larger datasets, as they require exhaustive distance calculations or complex computations like Voronoi diagrams, which are time and processor-intensive.
Innovation Solution
The implementation of interval content addressable memory (iCAM) systems that allow for parallel lookup of features using interval encoding, enabling fast and power-efficient searches by utilizing bounding boxes and efficient encoding schemes, such as ternary CAM, RAM, or binary search trees, to find best-matched features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive distance calculations or Voronoi diagrams are used for nearest neighbor searching, then search accuracy is improved, but processing time and power consumption increase significantly
Solution Approach 1:
The high-dimensional feature space is divided into multiple intervals along each dimension. Instead of calculating distances to all features, the search is segmented into interval-based lookups that narrow down candidate features quickly, reducing the number of distance calculations needed while maintaining search accuracy.
Solution Approach 2:
The patent transforms the traditional distance-based search into an interval-based search by introducing interval encoding along each dimension. This dimensional transformation allows the system to use bounded interval comparisons instead of exhaustive distance calculations, significantly reducing processing time while preserving search accuracy.
2Measurement precision
If exhaustive distance calculations or Voronoi diagrams are used for nearest neighbor searching, then search accuracy is improved, but power consumption increases significantly
Solution Approach 1:
The feature space is segmented into intervals along each dimension, allowing the search to be performed in discrete steps rather than continuous distance calculations. This segmentation reduces the computational workload and corresponding power consumption while maintaining the ability to accurately identify nearest neighbors.
Solution Approach 2:
The patent extracts only the essential interval boundaries needed for searching, rather than computing all pairwise distances. By taking out only the necessary interval information from the full feature space, the system reduces power consumption while preserving search accuracy.
3Measurement precision
If traditional nearest neighbor searching methods are used, then comprehensive feature matching is achieved, but device complexity and computational requirements increase
Solution Approach 1:
The complex high-dimensional search problem is segmented into simpler one-dimensional interval comparisons along each dimension. This segmentation reduces computational complexity by breaking down the problem into manageable pieces that can be processed independently and combined efficiently.
Solution Approach 2:
The patent changes the search approach from distance-based continuous computation to interval-based discrete lookup. This dimensional change simplifies the computational model, reducing device complexity while maintaining comprehensive feature matching capability through the interval encoding scheme.
Data Source
AI summary
Detailed herein are embodiments of systems, methods, and apparatuses to be used for feature searching using an entry-based searching structure.