Automated Neural Architecture Search for Design Optimization
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Solution Overview
Problem
The manual design of neural network architectures for machine learning systems is time-consuming and inefficient, as it requires substantial human effort and can be unfeasible due to the vast number of possible design choices, leading to suboptimal performance and high computational costs in automated neural architecture search (NAS) processes.
Innovation Solution
An automated neural architecture search (NAS) process using evolutionary algorithms (EA) and reinforcement learning (RL) to optimize neural network design parameters such as layer width, weight quantization, operator type, and network depth, which selects candidate design options based on computed gradient functions and loss functions to determine optimal neural network architectures for inference engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual design of neural network architectures is used, then human expertise can guide design decisions, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables automated neural architecture search where the computational system performs design optimization autonomously using evolutionary algorithms and reinforcement learning, eliminating the need for manual human intervention in the iterative design process while maintaining high-quality results
Solution Approach 2:
The patent replaces manual human design processes with automated computational algorithms (evolutionary algorithms and reinforcement learning), substituting human cognitive work with machine-based optimization systems that can evaluate vast design spaces efficiently
2Adaptability or versatility
If the search space for neural network designs is expanded to include more design choices, then better performance can be achieved, but the computational cost increases
Solution Approach 1:
The patent segments the design space into hierarchical levels (operator type, layer configuration, network depth, quantization parameters) and processes them through structured search algorithms, allowing comprehensive exploration of design options while managing computational complexity through organized evaluation
Solution Approach 2:
The system dynamically adjusts search parameters and evaluation criteria based on progress through the design space, modifying computational effort allocation to focus on promising regions while maintaining flexibility to explore diverse architectural configurations
3Manufacturing precision
If automated neural architecture search is used to explore more design options, then optimal solutions can be found, but the process requires substantial computational resources
Solution Approach 1:
The patent employs strategies that evaluate a selective subset of design candidates at full detail while using surrogate models or approximate evaluations for other candidates, achieving high optimization precision for critical architectures without exhaustively evaluating every possible design variant
Solution Approach 2:
The system introduces intermediary evaluation mechanisms (such as surrogate models, gradient-based approximations, or co-design frameworks) that bridge the gap between comprehensive search and computational feasibility, enabling accurate architecture optimization with reduced resource requirements
Data Source
AI summary
Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to determine options for decisions in connection with design features of a computing device. In a particular implementation, design options for two or more design decisions of neural network processing device may be identified based, at least in part, on combination of a definition of available computing resources and one or more predefined performance constraints.