ML Architecture Search Framework for Hardware-Aware Multi-Metric Optimization
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Solution Overview
Problem
Existing machine learning (ML) model optimization techniques are time-consuming, resource-intensive, and limited in applicability and performance metric scope, often failing to provide optimal architectures for diverse hardware platforms and tasks.
Innovation Solution
A comprehensive ML architecture search system that automatically discovers optimal ML architectures for specified AI/ML domains and hardware platforms, using a multi-metric approach to reduce search time and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ML model design techniques are used, then model accuracy can be achieved, but the process is time-consuming and resource-intensive
Solution Approach 1:
The framework performs preliminary actions by pre-defining architectural components, operations, and their configurations before the actual model design. It establishes a library of valid operations and their hyperparameter ranges in advance, allowing the search process to proceed more efficiently without needing to explore the entire design space from scratch.
Solution Approach 2:
The model design process is segmented into discrete architectural components and operations that can be independently selected and combined. The framework breaks down the complex task of designing an entire ML architecture into smaller units (layers, operations, configurations) that can be systematically searched and assembled.
2Extent of automation
If Neural Architecture Search (NAS) algorithms are used, then automatic architecture discovery is achieved, but computational resources and time are significantly increased
Solution Approach 1:
The framework changes the parameters of the search process by defining specific hyperparameter ranges and operational constraints for each architectural component. By parameterizing the search space with meaningful constraints (e.g., valid operation sets, hyperparameter ranges), it reduces the effective search space while maintaining automation.
Solution Approach 2:
Instead of exhaustively searching the entire architectural space, the framework uses partial action by focusing the search on promising regions defined by the operational library and constraints. It evaluates a subset of candidate architectures that are more likely to be optimal, reducing computational resources while maintaining effectiveness.
3Reliability
If NAS is applied to find optimal architectures, then performance on target task is improved, but the process requires many hours or days to train a single network
Solution Approach 1:
The framework uses copying by creating a library of pre-defined operational templates and architectural patterns that can be replicated and recombined. Instead of training entirely new architectures from scratch for each target task, it copies and adapts proven operational building blocks, significantly reducing training duration while maintaining performance.
Solution Approach 2:
Architectural components and operations are prepared in advance with their hyperparameter ranges and validity constraints pre-established. This preliminary preparation allows the actual architecture search and training to proceed much faster, as the framework only needs to combine pre-valid components rather than explore and validate every possible configuration.
4Loss of time
If proxy functions are used to predict model accuracy, then training time is reduced, but correlation to actual performance metric is insufficient
Solution Approach 1:
The framework incorporates feedback mechanisms where the actual performance of candidate architectures on the target task is evaluated and used to refine the search process. Performance feedback from training and validation loops informs subsequent architectural selections, ensuring that proxy predictions are continuously aligned with actual performance metrics.
Data Source
AI summary
The present disclosure is related to framework for automatically and efficiently finding machine learning (ML) architectures that are optimized to one or more specified performance metrics and/or hardware platforms. This framework provides ML architectures that are applicable to specified ML domains and are optimized for specified hardware platforms in significantly less time than could be done manually and in less time than existing ML model searching techniques. Furthermore, a user interface is provided that allows a user to search for different ML architectures based on modified search parameters, such as different hardware platform aspects and/or performance metrics. Other embodiments may be described and/or claimed.