Superposition Model Selection for Task- and Hardware-Specific Deployment
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Solution Overview
Problem
Foundation models, despite their universality, are not hardware-efficient due to large parameter sizes and high computational and memory costs, making them difficult to deploy in real-world settings, and existing knowledge-transfer methods incur significant computational costs when adapting to new hardware architectures.
Innovation Solution
A trained superposition model is used, pre-trained in a task-agnostic manner, which is then finetuned for application tasks in a hardware-architecture-agnostic way, and a machine learning model is selected from this model based on performance and hardware characteristics using a search that considers both application and computational performance, allowing adaptation to new hardware architectures with reduced computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If foundation models are used for their universality and broad application capability, then adaptability to different tasks is improved, but hardware efficiency deteriorates due to large parameter sizes and high computational costs
Solution Approach 1:
The patent segments the foundation model into multiple specialized models, each optimized for specific tasks and hardware architectures. Instead of deploying a single large foundation model across all tasks, the system divides it into task-specific components that can be independently trained and deployed on appropriate hardware, reducing the computational burden while maintaining versatility.
Solution Approach 2:
The patent implements dynamic model selection and adaptation mechanisms that allow the system to choose appropriate model configurations based on task requirements and hardware capabilities. This dynamic approach enables the system to switch between different model sizes and complexities, optimizing computational resource usage while maintaining adaptability across diverse tasks.
2Ease of manufacture
If knowledge-transfer methods are used to compress foundation models for deployment on target hardware, then hardware efficiency is improved, but significant computational costs are incurred when adapting to new hardware architectures
Solution Approach 1:
The patent performs preliminary actions by pre-training specialized models on diverse tasks and hardware configurations before deployment. This advance preparation creates a library of pre-adapted models that can be quickly selected and deployed on target hardware without requiring extensive retraining, thus reducing deployment time while maintaining hardware efficiency.
Solution Approach 2:
The patent utilizes parameter changes by adjusting model architecture parameters, capacity, and complexity based on target hardware specifications. Instead of retraining entire models from scratch, the system modifies key parameters such as layer depths, filter sizes, and activation functions to optimize models for specific hardware constraints, significantly reducing adaptation time.
3Adaptability or versatility
If foundation models are deployed in real-world settings, then broad task coverage is achieved, but model size and parameter count increase making deployment difficult
Solution Approach 1:
The patent segments the large foundation model into multiple smaller, task-specific models. Each segmented model covers a specific subset of tasks with reduced parameter counts, making them easier to deploy on resource-constrained devices while collectively maintaining broad task coverage through the ensemble of specialized models.
Solution Approach 2:
The patent extracts only the necessary components and parameters from the foundation model that are relevant to specific tasks and hardware platforms. By taking out and retaining only the essential elements needed for particular applications, the system reduces overall model size while preserving task coverage capabilities.
Data Source
AI summary
A system and method for providing a task- and hardware-architecture-specific machine learning model. A trained superposition model is provided, which includes a superposition of a set of machine learning models, individual ones of the set of machine learning models being extractable from the trained superposition model. A characterization of a target hardware architecture is received. The trained superposition model is finetuned for an application task in a hardware-architecture-agnostic way. A machine learning model is selected from the finetuned superposition model, the selecting including, for the target hardware architecture, a search using a first function describing a first performance of a candidate machine learning model for the application task and a second function describing a second performance of the candidate machine learning model when executed on the target hardware architecture. The selected machine learning model is provided as output for deployment on the target hardware architecture.