Inference Cache Tree for Low-Latency Machine Learning Predictions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern machine learning models, particularly ensemble trees, require significant computational resources and increased inference time, leading to bottlenecks in low-latency operations due to their complexity and computational overhead.
Innovation Solution
An inference cube system utilizing a data cache with a self-learning tree structure that dynamically maintains tree data based on computational restrictions, reducing computational requirements and enabling fast inference searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ensemble tree models are used for machine learning inference, then prediction accuracy is improved, but computational inference time increases and latency increases
Solution Approach 1:
The patent segments the inference process into two distinct paths: a fast cache lookup path for common patterns and a full model inference path for complex cases. The inference cache stores pre-computed results for frequently encountered input patterns, allowing the system to return predictions without executing the complete ensemble tree model, thus reducing inference time while maintaining accuracy for cached patterns.
Solution Approach 2:
The system performs preliminary computation by pre-processing training data into decision rules and storing them in an inference cache during offline training. This preliminary action creates a lookup table of decision rules that can be quickly applied during inference, avoiding the need to execute the full computational model for every prediction request.
2Measurement precision
If ensemble tree models with thousands of base models are used, then prediction accuracy is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent extracts the essential decision-making logic from the complex ensemble tree model and stores it as a separate inference cache with pre-computed decision rules. This extraction separates the heavy computational model from the inference process, allowing the model structure to remain complex for accuracy while the inference mechanism becomes simpler and faster by relying on cached rules.
Solution Approach 2:
The system creates a simplified copy of the model's decision logic in the form of an inference cache containing decision rules derived from the training data. This copy enables fast inference by allowing the system to lookup predictions based on cached rules rather than executing the full complex model, thus reducing computational complexity during inference while preserving prediction accuracy.
3Measurement precision
If full model inference is performed for every request, then prediction accuracy is maintained, but productivity and throughput decrease
Solution Approach 1:
The inference process is segmented into cache lookup operations for common cases and full model inference for rare cases. By segmenting the workload, the system can handle the majority of requests through fast cache lookups, reserving resource-intensive full model inference only for cases where the cache does not contain a matching pattern, thereby increasing overall throughput while maintaining accuracy.
Solution Approach 2:
The system performs only a partial inference operation by first checking the cache for matching patterns before committing to full model inference. This partial action (cache lookup) is sufficient for cached patterns, eliminating the need for excessive full model execution in those cases and improving overall productivity while maintaining accuracy when cache hits occur.
Data Source
AI summary
One or more computer processors calculate a cache prediction for a received inference request within an inference cache structured as a self-learning tree, wherein the inference request comprises a set of input values. The one or more computer processors responsive to the retrieved cache prediction exceeding a cache prediction threshold, transmit the cache prediction. The one or more computer processors parallel compute a model prediction for the received inference request utilizing a trained model. The one or more computer processors responsive to the retrieved model prediction exceeding a model prediction threshold, convert the trained model into a tree structure. The one or more computer processors update the inference cache with the converted train model. The one or more computer processors transmit the model prediction.