ML Inference Process Selection Under Memory and Latency Constraints
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Solution Overview
Problem
Machine learning models, particularly deep neural networks, are memory-intensive and computationally expensive, hindering their deployment in devices with low memory resources and strict latency or cost considerations, and current methods for selecting an inference process are time-consuming and inefficient.
Innovation Solution
A model deployment tuning system (MDTS) that identifies suitable model utilization techniques and compute instance types using model compression and visibility into network configurations to optimize deployment, reducing latency and cost while maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models with numerous hidden neural network layers are deployed, then model performance and accuracy are improved, but memory consumption and computational cost increase significantly
Solution Approach 1:
The patent segments the machine learning model into multiple inference processes with different computational characteristics. By dividing the model into distinct inference pathways (e.g., different numbers of neural network layers, different computation intensities), the system enables selective deployment of model components based on available resources, thus reducing memory consumption while maintaining necessary accuracy for specific tasks.
Solution Approach 2:
The patent changes key parameters of the inference process including the number of neural network layers, computation precision, and resource allocation. By dynamically adjusting these parameters based on device capabilities and task requirements, the system achieves optimal balance between model accuracy and memory consumption, allowing deployment on devices with limited resources.
2Measurement precision
If machine learning models with numerous hidden neural network layers are deployed, then model performance is improved, but computational cost and latency increase
Solution Approach 1:
The patent implements dynamic inference processes that can adaptively adjust the number of neural network layers and computation intensity based on real-time requirements. This dynamic approach allows the system to use full model depth when accuracy is critical and reduce depth when latency is the priority, thereby resolving the contradiction between model performance and inference speed.
Solution Approach 2:
The patent applies partial action by selectively executing only the necessary number of neural network layers required for a given task. Instead of always running the complete deep model, the system determines the minimum sufficient computation depth needed, reducing unnecessary computational overhead and latency while maintaining adequate accuracy for the specific application.
3Adaptability or versatility
If traditional inference process selection methods are used, then deployment flexibility is maintained, but time consumption for identifying suitable inference processes increases
Solution Approach 1:
The patent performs preliminary characterization of the machine learning model and pre-evaluates multiple inference processes before deployment. By conducting model analysis, resource requirement assessment, and inference process benchmarking in advance, the system builds a knowledge base that enables rapid selection of suitable inference processes during deployment, significantly reducing selection time while maintaining deployment flexibility across different devices and scenarios.
Data Source
AI summary
A model deployment tuning system (MDTS) receives a trained ML model, specified constraints, and model evaluation data and applies a plurality of model utilization techniques to the trained ML model to produce a plurality of useable model versions of the trained ML model. The MDTS executes each of the plurality of useable model versions of the trained ML models on a plurality of different compute instance types using the model evaluation data to produce model evaluation results for a plurality of different combinations. The MDTS filters the model evaluation results based on the specified constraints to indicate one or more of the different combinations satisfying the specified constraints. The MDTS deploys one of the plurality of useable model versions of the trained ML model to a compute instance types according to a selected combination satisfying the specified constraints.