Neural Network Pruning for Accuracy and Efficiency
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Solution Overview
Problem
Conventional automated machine learning systems for neural networks are inefficient, inflexible, and prone to inaccuracies due to high computational requirements, inability to adapt to different tasks, and reliance on human-designed architectures.
Innovation Solution
The implementation of a neural network architecture pruning system that progressively trains and updates the neural network structure using pruning parameters to reduce its size while maintaining accuracy, allowing it to adapt to various tasks and operate on resource-constrained devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks are used to perform tasks with high accuracy, then the accuracy is improved, but the computational resources and memory requirements increase significantly
Solution Approach 1:
The patent extracts and removes redundant or less important components from the neural network architecture. This is achieved through automated machine learning techniques that identify and eliminate unnecessary neurons, layers, or connections while preserving the network's essential functionality and accuracy, thereby reducing computational resource requirements
Solution Approach 2:
The patent changes key parameters of the neural network architecture through automated optimization. The system adjusts parameters such as the number of layers, neurons per layer, learning rates, and other hyperparameters to achieve optimal performance with reduced computational resources, transforming the network structure to balance accuracy and efficiency
2Adaptability or versatility
If automated machine learning systems use architecture search to discover optimal operations, then the adaptability is improved, but the computational power requirements increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training neural networks on large datasets before deployment. This pre-training phase establishes a foundation of learned operations and representations that can be adapted to specific tasks with minimal additional computational resources, reducing the need for extensive architecture search at deployment time
Solution Approach 2:
The patent creates universal neural network architectures that can perform multiple tasks through transfer learning and fine-tuning. The system designs networks with modular components and reusable operations that can be adapted to different tasks by adjusting parameters rather than redesigning the entire architecture, thereby reducing computational requirements for task-specific optimization
3Ease of manufacture
If conventional systems rely on human-designed neural network architectures, then the ease of manufacture is improved, but the accuracy and adaptability deteriorate
Solution Approach 1:
The patent implements self-service through automated machine learning systems that automatically design, optimize, and configure neural network architectures without human intervention. The system uses algorithms to search the architecture space, evaluate performance, and select optimal configurations autonomously, eliminating the need for expert human designers while achieving superior accuracy and adaptability
Data Source
AI summary
The disclosure describes one or more implementations of a neural network architecture pruning system that automatically and progressively prunes neural networks. For instance, the neural network architecture pruning system can automatically reduce the size of an untrained or previously-trained neural network without reducing the accuracy of the neural network. For example, the neural network architecture pruning system jointly trains portions of a neural network while progressively pruning redundant subsets of the neural network at each training iteration. In many instances, the neural network architecture pruning system increases the accuracy of the neural network by progressively removing excess or redundant portions (e.g., channels or layers) of the neural network. Further, by removing portions of a neural network, the neural network architecture pruning system can increase the efficiency of the neural network.