Inference Model Parameter Sharing for GPU Memory-Constrained Deployment
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Solution Overview
Problem
Conventional deep learning frameworks fail to differentiate between immutable and mutable parameters in AI model management, leading to inefficient memory and computing resource utilization in edge computing environments, particularly in GPU deployments, which restricts the number of inference application instances and compromises real-time service quality.
Innovation Solution
Implement a memory management technique that distinguishes between immutable and mutable parameters, allocating shared GPU memory for immutable parameters and local memory for mutable parameters, leveraging CUDA features for efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple inference application instances are deployed in a computing node to satisfy real-time requirements, then real-time service quality is improved, but memory capacity restrictions are exceeded leading to resource waste
Solution Approach 1:
The patent merges the storage of immutable model parameters across multiple inference instances by creating a shared memory space. Instead of each instance maintaining separate copies of pre-trained parameters, the system combines them into a single shared repository that all instances can access, thereby reducing total memory consumption while maintaining real-time service capabilities.
Solution Approach 2:
The shared memory space serves multiple inference application instances simultaneously, making it a universal resource. This multi-functional memory structure allows the same memory region to be used by numerous instances for their common immutable parameters, optimizing memory utilization and enabling deployment of more instances within the same memory constraints.
2Device complexity
If deep learning frameworks do not differentiate between immutable and mutable model parameters, then framework simplicity is maintained, but memory and computing resource utilization becomes inefficient
Solution Approach 1:
The patent segments model parameters into two distinct categories: immutable pre-trained parameters and mutable instance-specific parameters. This segmentation allows the system to apply different memory management strategies to each type - shared storage for immutable parameters and local storage for mutable ones - thereby improving resource utilization efficiency while maintaining reasonable framework complexity through clear categorization.
Solution Approach 2:
The patent applies local quality by treating different types of parameters differently based on their characteristics. Immutable parameters receive shared memory allocation while mutable parameters receive local memory allocation. This differentiated approach optimizes resource utilization by matching the storage strategy to the specific needs of each parameter type, rather than applying a uniform approach to all parameters.
Data Source
AI summary
Techniques for model parameter sharing between inference model instances are disclosed. For example, a method performed by a first process obtains a representation of an inference model for which multiple instances of the inference model are to be executed on at least one processing unit. The method determines, from the representation of the inference model, one or more model parameters that are a pre-trained type of model parameter. The method allocates a shared memory for storing the one or more model parameters that are the pre-trained type of model parameter. The method stores the one or more model parameters that are the pre-trained type of model parameter in the shared memory for access by the multiple instances of the inference model to be executed on the at least one processing unit.