Ensemble Model Inference Acceleration via Convergence Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning inference processes, particularly in ensemble models like random forest and gradient boosting, are resource-intensive and time-consuming due to the need for completing all tasks, even when convergence is likely achieved.
Innovation Solution
A method that schedules tasks for base learners and evaluates an exit condition using a deterministic function of task outcomes to determine convergence, allowing for early interruption of task execution and estimation of the ensemble model's inference result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all tasks are executed for base learners in ensemble models, then prediction accuracy is improved, but execution time and computational resources are excessive
Solution Approach 1:
The patent applies preliminary action by evaluating an exit condition before completing all task executions. The deterministic function assesses whether sufficient task outcomes have been obtained to estimate the ensemble model's inference result, allowing early termination of task execution while maintaining acceptable prediction accuracy
Solution Approach 2:
The patent implements partial action by executing only a subset of scheduled tasks rather than all tasks. The system determines that a sufficient number of task outcomes have been obtained when the exit condition is met, allowing the ensemble model inference to proceed with partial task completion, thus reducing execution time while maintaining adequate accuracy
2Measurement precision
If all tasks are executed for base learners in ensemble models, then prediction accuracy is improved, but computational resources are excessive
Solution Approach 1:
The system performs preliminary evaluation of the exit condition during task execution. The deterministic function assesses whether the obtained task outcomes are sufficient for inference result estimation, enabling early termination that conserves computational resources while maintaining acceptable prediction accuracy
Solution Approach 2:
The patent applies partial action by completing only the necessary subset of tasks required to meet the exit condition. This approach reduces computational resource consumption by avoiding execution of redundant tasks beyond what is needed for adequate inference result estimation
3Productivity
If tasks are interrupted early based on convergence determination, then execution time is reduced, but prediction accuracy may be compromised
Solution Approach 1:
The patent implements feedback through the deterministic function that continuously evaluates the exit condition based on obtained task outcomes. This feedback mechanism monitors convergence and determines when sufficient accuracy has been achieved, allowing early task interruption while maintaining prediction accuracy within acceptable bounds
Solution Approach 2:
The system changes the parameter of task execution completeness by transitioning from executing all scheduled tasks to executing only until the exit condition is met. The deterministic function dynamically determines the sufficient number of task outcomes needed, adjusting the execution scope based on convergence assessment
Data Source
AI summary
A method is provided for accelerating machine learning inferences. The method uses an ensemble model run on input data. This ensemble model involves several base learners, where each of the base learners has been trained. The method first schedules tasks for execution. As a result of the task scheduling, one of the base learners is executed based on a subset of the input data. The execution of the tasks is then started to obtain respective task outcomes. An exit condition is repeatedly evaluated while executing the tasks by computing a deterministic function of the task outcomes obtained so far. This deterministic function output values indicate whether an inference result of the ensemble model has converged. Accordingly, the execution of the tasks can be interrupted if the exit condition evaluated last is found to be fulfilled. Eventually, an inference result of the ensemble model is estimated based on the task outcomes.