Inference Cache for Machine Learning Model Attribute Data
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Solution Overview
Problem
The loading of attribute data for machine learning models is a time-consuming operation, which can reduce the rate at which inferences can be performed and may require reloading previously displaced attribute data, leading to performance delays.
Innovation Solution
The implementation of an inference cache that stores attribute data of trained machine learning models, allowing for rapid retrieval and reducing the need for repeated loading, by using a controller associated with the inference cache to store and manage attribute data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If attribute data is loaded from main memory for each inference, then the inference can be performed with complete data, but the time required for inference increases due to slow memory access
Solution Approach 1:
The patent pre-loads attribute data (weights, biases, structure) into a specialized cache memory before inference operations are needed. This preliminary action ensures that when inference requests arrive, the required data is already available in fast cache memory rather than being fetched from slow main memory at the moment of need, thus resolving the time penalty without compromising data completeness
Solution Approach 2:
The patent introduces a specialized cache memory structure as an intermediary between main memory and the inference processing unit. This cache acts as a buffer that stores frequently accessed attribute data, mediating between the slow main memory and the fast inference engine, thereby reducing access time while maintaining data integrity
2Speed
If attribute data is cached in memory, then the retrieval speed increases, but the device complexity increases due to additional cache management
Solution Approach 1:
The patent implements a specialized cache memory with specific characteristics optimized for machine learning attribute data storage and retrieval. Rather than using generic memory management, the cache is designed with local qualities tailored to the access patterns and data structures of ML models, achieving high speed while keeping management complexity contained within the specialized component
Solution Approach 2:
The patent segments the attribute data into distinct components (weights, biases, structural parameters) and manages them separately in the cache. This segmentation allows for efficient organization and retrieval of different data types without requiring complex unified management, simplifying the overall cache management architecture while maintaining high retrieval speeds
Data Source
AI summary
In some implementations, a controller may receive a request for an inference. The controller may determine, based on the received request for the inference, a first inference model of a plurality of inference models, to generate the inference. The controller may obtain, from a memory associated with an inference cache, first attribute data regarding first attributes of the first inference model. A location of the first attribute data, in the memory, may be determined using the inference cache. The attributes may include weights associated with the first inference model, biases associated with the first inference model, and a structure of the first inference model. The controller may utilize the first attribute data to generate the inference based on the request.