Gradient Boosting Branch Score Scaling Circuit
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Gradient boosting algorithms face challenges in maintaining discrimination performance while preventing an increase in circuit scale, particularly when calculating branch scores for large datasets, due to the need for increased bit width and circuit complexity.
Innovation Solution
A learning device and method that includes a branch score calculator and a scaling unit to perform scaling on cumulative gradient information, allowing branch scores to be calculated within a manageable numerical range, thereby maintaining performance without escalating circuit complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the bit width of the accumulated gradient is increased to maintain discrimination performance with large datasets, then the accuracy is improved, but the circuit scale increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the bit width of the accumulated gradient based on the actual data characteristics and calculation requirements. Instead of using a fixed large bit width for all datasets, the system adapts the precision parameter to match the specific problem, maintaining discrimination performance while avoiding unnecessary circuit complexity.
Solution Approach 2:
The patent introduces dynamic adjustment of the accumulated gradient bit width during the gradient boosting process. The bit width is not static but changes according to the stage of learning and the characteristics of the data being processed, allowing the circuit to optimize its resource usage while maintaining accuracy.
2Reliability
If gradient boosting is performed multiple times for hyperparameter adjustment and model ensemble, then the generalization performance is improved, but the calculation time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing gradient information in an optimized format before the actual gradient boosting iterations. This preprocessing step reduces the computational burden during repeated hyperparameter adjustments and model ensemble operations, significantly reducing calculation time while preserving generalization performance.
Solution Approach 2:
The patent extracts and stores essential gradient information in a compressed or pre-processed form, separating the critical computational elements from the full dataset. This allows rapid retrieval and reuse during multiple gradient boosting passes, reducing redundant calculations and accelerating the overall process.
Data Source
AI summary
A learning device is configured to perform learning of a decision tree. The learning device includes a branch score calculator, and a scaling unit. The branch score calculator is configured to calculate a branch score used for determining a branch condition for a node of the decision tree based on a cumulative sum of gradient information corresponding to each value of a feature amount of learning data. The scaling unit is configured to perform scaling on a value related to the cumulative sum used for calculating the branch score by the branch score calculator to fall within a numerical range with which the branch score is capable of being calculated.