Decision Tree Node Alignment for Cache Efficiency
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Solution Overview
Problem
The frequent transitions between nodes in decision tree-based discrimination processes lead to decreased cache hit ratios and processing slowdowns due to distant memory accesses, as existing methods do not efficiently align nodes in memory.
Innovation Solution
An apparatus and method that aligns decision tree nodes in memory using a combination of breadth-first and depth-first orders based on a threshold, optimizing node placement to improve cache efficiency and reduce memory access time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nodes are stored in memory using conventional storage methods, then the decision tree can be implemented, but cache hit ratio decreases due to distant memory accesses between frequently transitioned nodes
Solution Approach 1:
The patent applies preliminary action by pre-aligning nodes in memory according to their depth and breadth in the decision tree structure before the discrimination process begins. This pre-arrangement ensures that frequently accessed nodes during traversal are stored in contiguous or nearby memory locations, eliminating the need for distant memory accesses during actual operation and thereby improving cache hit ratio and processing speed
2Productivity
If nodes are aligned in memory without optimization, then implementation is simple, but memory access distances increase causing process slowdowns
Solution Approach 1:
The patent applies segmentation by dividing nodes into different groups based on their depth from the root node. Nodes at the same depth level are aligned together in memory, creating segmented regions that correspond to different levels of the decision tree. This segmentation strategy ensures that during traversal, nodes accessed at similar depths are located nearby in memory, reducing access distances and improving processing efficiency while maintaining a systematic and manageable alignment approach
Data Source
AI summary
An apparatus for discrimination includes a memory, an alignment unit configured to align nodes of a decision tree in the memory, wherein a node in which depth from a root node is not greater than a threshold is aligned in accordance with a breadth of first order, and a node in which depth from a root node is greater than a threshold is aligned in accordance with a depth of first order, and a discrimination unit, receiving an inputted data, configured to discriminate a class of the inputted data using the nodes aligned in the memory.