Inference Result Caching for Machine Learning Response Time
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Solution Overview
Problem
Machine learning models, especially those used in large AI systems, often require significant computation time for inference, leading to long response times and performance issues, particularly when provided as a remote service, which can impede their integration into software systems and impact overall system performance.
Innovation Solution
The method involves collecting and storing inference results and associated inputs, allowing for approximate matching using techniques like nearest neighbor search or approximate string matching to generate an approximation of the result for new inference requests, reducing the time required to obtain inference results without increasing the model's speed, and enabling the use of more elaborate models for more accurate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used for inference in production environments, then accurate predictions and decisions can be made, but computation time increases leading to long response times
Solution Approach 1:
The patent applies preliminary action by collecting and storing inference results from previous executions before they are needed. When a new inference request arrives, the system first checks if similar inputs have been processed before and stores their results in a cache. This pre-computed cache allows the system to quickly retrieve and return stored results for matching inputs, avoiding the need to re-execute the full machine learning model and significantly reducing response time while maintaining inference accuracy.
2Reliability
If the machine learning model processes every inference request, then accurate results are obtained, but system performance decreases due to repeated computation
Solution Approach 1:
The patent applies copying by creating and storing copies of inference results from previous executions. Instead of re-processing identical or similar inputs through the full machine learning model, the system checks its cache for matching inputs and returns the stored copy of the inference result. This copying mechanism maintains reliability by ensuring accurate results are returned while dramatically improving productivity by avoiding redundant computation and enabling the system to handle more inference requests per unit time.
3Adaptability or versatility
If machine learning models are provided as remote services, then flexibility and scalability are improved, but network latency and response time increase
Solution Approach 1:
The patent introduces an intermediary caching layer between the client and the remote machine learning model service. When inference requests are made, the system first queries this local cache intermediary to check if the result is already available. If found, the cached result is returned immediately without needing to communicate with the remote service, thus eliminating network latency for repeated requests. This intermediary approach maintains the flexibility and scalability of remote service deployment while significantly reducing network response time for common inference scenarios.
Data Source
AI summary
Inference results of a machine learning model and associated inputs are collected. An inference request is received. A determination is made whether a request input of the inference request matches at least one collected input of a set of collected inputs. In response to determining that the request input matches at least one collected input in the set of collected inputs, an inference result is determined using one or more collected inference results associated with said one or more matching inputs in the set of collected inputs.