Neural Architecture Search for Accuracy-Latency Tradeoffs
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Solution Overview
Problem
Existing neural network architecture search methods are tedious, costly, and often rely on trial-and-error, particularly when dealing with exponentially large hyperparameter spaces, and current automated approaches introduce bias or require accurate approximations of non-differentiable terms.
Innovation Solution
A unified framework for neural architecture search (UNAS) that bridges differentiable and reinforcement-learning-based methods, using gradient estimators with lower variance and allowing non-differentiable objective functions, such as network latency, while optimizing for generalization error to prevent overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If trial-and-error methods are used to search for neural network architecture, then the search can be performed manually, but the process becomes tedious and costly
Solution Approach 1:
The patent replaces manual trial-and-error mechanical search with an automated reinforcement learning system. The RL agent automatically explores the architecture search space by selecting operations and hyperparameters, eliminating the need for manual intervention while significantly reducing search time compared to traditional methods.
Solution Approach 2:
The system enables self-service architecture search where the RL agent autonomously performs the entire architecture search process without human intervention. The agent learns optimal architectures through self-exploration and optimization, making the search process independent and automated.
2Productivity
If automated architecture search methods are used, then the search process becomes efficient, but bias is introduced or accurate approximations of non-differentiable terms are required
Solution Approach 1:
The patent introduces a reward model as an intermediary that bridges the RL agent and the actual architecture evaluation. This reward model provides accurate feedback signals to guide the RL agent's search, enabling efficient exploration without introducing bias. The reward model accurately estimates architecture quality, ensuring reliable search results while maintaining high efficiency.
3Adaptability or versatility
If the search space is expanded to include more operations and hyperparameters, then the architecture performance can be improved, but the search complexity increases exponentially
Solution Approach 1:
The patent segments the architecture search space into manageable components: operation selection, hyperparameter optimization, and architecture assembly. The RL agent handles operation and hyperparameter selection while a separate architecture assembly process combines these elements. This segmentation makes the exponentially large search space tractable by breaking it into smaller, more manageable sub-problems.
Data Source
AI summary
In at least one embodiment, differentiable neural architecture search and reinforcement learning are combined under one framework to discover network architectures with desired properties such as high accuracy, low latency, or both. In at least one embodiment, an objective function for search based on generalization error prevents the selection of architectures prone to overfitting.