Neural Network Architecture Construction via Joint Mask Training
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Solution Overview
Problem
Traditional Neural Architecture Search (NAS) processes are inefficient and costly due to their multi-stage training, evaluation, and search methods, which consume significant computational resources and lack deployment efficiency across different hardware platforms.
Innovation Solution
The use of a multi-dimensional performance metric mask is introduced to jointly train neural network architectures, updating performance metrics during training and simulation, allowing for the selection of candidate architectures that meet specific accuracy and latency requirements, and subsequent calibration for efficient deployment on target hardware platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multi-stage NAS processes are used to select neural network architectures, then architecture performance requirements (accuracy, latency) can be met, but computational resource consumption and training time increase significantly
Solution Approach 1:
The patent combines multiple separate NAS stages (pretraining, training, evaluation, search) into a unified end-to-end training process. The controller network and architecture selection are integrated within the same training loop, allowing simultaneous optimization of network parameters and architecture selection without separate evaluation stages, thereby reducing training time while maintaining performance requirements.
Solution Approach 2:
The controller network is pre-trained to predict optimal architecture configurations before actual network training. This preliminary action allows the system to identify promising architecture candidates in advance, reducing the search space and subsequent training time required to meet performance requirements.
2Reliability
If traditional multi-stage NAS processes are used to select neural network architectures, then architecture performance requirements (accuracy, latency) can be met, but computational resource consumption increases significantly
Solution Approach 1:
The patent merges architecture search and network training into a single integrated process. The controller network guides architecture selection while the main network trains simultaneously, eliminating redundant computational stages and reducing overall computational resource consumption while still meeting accuracy and latency requirements.
Solution Approach 2:
The controller network autonomously learns to select optimal architecture configurations through self-supervised learning from training data, without requiring external evaluation or manual tuning. This self-service mechanism reduces computational overhead by eliminating separate evaluation stages and automated hyperparameter tuning processes.
3Adaptability or versatility
If traditional NAS processes are used, then neural network architectures can be selected, but deployment efficiency across different hardware platforms is lacking
Solution Approach 1:
The controller network is designed with multi-functionality to handle both architecture selection and performance prediction across different hardware platforms. By integrating platform-specific performance metrics into the unified training objective, the system achieves adaptability to multiple deployment environments without requiring separate NAS processes for each platform.
Solution Approach 2:
The patent incorporates hardware-specific parameters (such as latency and accuracy requirements for different platforms) as dynamic constraints in the unified training objective. This allows the same NAS process to adapt to different hardware platforms by changing optimization parameters rather than requiring separate complex processes for each platform.
Data Source
AI summary
Apparatuses, systems, and techniques to construct a neural network architecture. In at least one embodiment, candidate neural components may be selected for the neural network by jointly updating performance metric masks attached to these candidate neural components and a union neural network comprising all candidate neural components.