Machine Learning Model Scaling via Similarity-Based Swapping
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Solution Overview
Problem
Existing ML model optimization techniques are time-consuming, resource-intensive, and fail to efficiently adapt to varying hardware platforms and environmental conditions, leading to suboptimal performance for users with different hardware and performance metric preferences.
Innovation Solution
A machine learning model scaling (MLMS) system that selects and deploys ML models in an energy and communication-efficient manner, using a similarity-based subnet selection process to minimize memory write operations and adapt to real-time system constraints, agnostic to specific ML model search methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ML models are optimized for different hardware platforms and performance metrics, then model performance and adaptability are improved, but the time and computational resources required for optimization increase significantly
Solution Approach 1:
The system pre-computes and stores performance metrics for multiple ML models across different hardware platforms before deployment. When a model swap is needed, the system retrieves pre-computed performance data to make rapid adaptation decisions without performing time-consuming optimization calculations in real-time.
Solution Approach 2:
The system dynamically selects and swaps between different ML models based on real-time hardware conditions and performance requirements. Instead of statically optimizing a single model for all scenarios, the system maintains a pool of pre-trained models with different characteristics and dynamically chooses the most appropriate one for current conditions.
2Measurement precision
If ML models are manually designed and trained for specific tasks, then model accuracy is improved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system uses a pool of pre-trained ML models that can be copied and deployed across different applications. Instead of manually designing and training new models for each task, the system selects from existing models that have been trained on various datasets and can be adapted through configuration rather than retraining.
Solution Approach 2:
The system creates ML models with universal applicability that can serve multiple tasks and hardware platforms. By designing models with flexible architectures and pre-training them on diverse data, the same model can be deployed across different applications with minimal customization, reducing the need for separate manual design processes.
3Reliability
If larger ML architectures are used to improve performance, then model capabilities are enhanced, but computational resources and training time increase
Solution Approach 1:
The system dynamically adjusts the complexity and size of ML models based on available computational resources and performance requirements. When resources are abundant, larger models with greater capabilities are deployed. When resources are constrained, smaller, more efficient models are selected from the pool, allowing the system to optimize the trade-off between performance and resource consumption in real-time.
Data Source
AI summary
The present disclosure is related to machine learning model swap (MLMS) framework for that selects and interchanges machine learning (ML) models in an energy and communication efficient way while adapting the ML models to real time changes in system constraints. The MLMS framework includes an ML model search strategy that can flexibly adapt ML models for a wide variety of compute system and/or environmental changes. Energy and communication efficiency is achieved by using a similarity-based ML model selection process, which selects a replacement ML model that has the most overlap in pre-trained parameters from a currently deployed ML model to minimize memory write operation overhead. Other embodiments may be described and/or claimed.