Neural Network Compression via Reinforcement Learning
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Solution Overview
Problem
Current techniques for compressing neural networks often compromise accuracy for improved performance, lacking efficient methods to achieve both desired accuracy and performance simultaneously.
Innovation Solution
A system utilizing a reinforcement learning model processes performance and accuracy policies to determine a combination strategy for compressing neural networks, employing techniques like pruning, quantization, and sparsity to achieve target metrics on various processing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are compressed using traditional techniques, then performance is improved, but accuracy deteriorates
Solution Approach 1:
The patent applies parameter changes by systematically varying compression parameters (pruning ratio, quantization bits, sparsity level) to find optimal configurations that simultaneously satisfy both performance and accuracy requirements. The reinforcement learning model learns the relationship between compression parameters and model quality metrics, enabling parameter optimization that resolves the contradiction between compression performance and model accuracy.
Solution Approach 2:
The patent implements dynamics by using a reinforcement learning model that adaptively determines compression strategies based on target performance and accuracy metrics. Rather than applying static compression rules, the system dynamically adjusts compression configurations through iterative learning, allowing the model to navigate the trade-off between performance improvement and accuracy preservation in a flexible, goal-directed manner.
2Quantity of substance
If compression techniques are applied to neural networks, then resource usage is reduced, but model quality deteriorates
Solution Approach 1:
The patent uses parameter changes to control model size while preserving quality by optimizing compression parameters such as pruning ratios and quantization precision. The reinforcement learning model learns to select parameter configurations that achieve target model sizes without excessive quality loss, resolving the contradiction between reducing model quantity and maintaining reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the reinforcement learning model receives feedback on model quality metrics and resource usage, then adjusts compression strategies accordingly. This closed-loop control enables the system to iterate toward solutions that simultaneously achieve desired model size reduction and quality preservation, rather than accepting quality deterioration as an inevitable trade-off.
3Productivity
If aggressive compression is applied, then deployment efficiency is improved, but accuracy is compromised
Solution Approach 1:
The patent applies dynamics by using reinforcement learning to adaptively determine the optimal level of compression aggression based on target accuracy and performance requirements. The system dynamically adjusts compression intensity, transitioning from aggressive to conservative strategies as needed, rather than applying fixed aggressive compression rules that inevitably compromise accuracy.
Solution Approach 2:
The patent uses parameter changes to control compression aggression by optimizing parameters such as pruning ratio, quantization precision, and sparsity level. The reinforcement learning model learns to select parameter configurations that achieve deployment efficiency improvements without excessive accuracy loss, resolving the contradiction between aggressive compression benefits and accuracy preservation.
Data Source
AI summary
Apparatuses, systems, and techniques to compress neural networks. In at least one embodiment, one or more first neural networks are used to cause one or more compressed neural networks to be selected based, at least in part, on accuracy and performance of the one or more compressed neural networks.