Neural Network Weight Sparsification and Quantization Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep neural network models face inefficiencies in resource utilization and accuracy when optimized, as conventional methods do not effectively balance compression and loss of accuracy during sparsification and quantization processes.
Innovation Solution
A neural network model optimizer employs sparsification and quantization techniques, using Hessian and gradient-based methods to selectively remove or quantify weight values, minimizing error and optimizing layers to achieve greater compression while maintaining model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sparsification and quantization are applied to optimize neural network models, then model compression is improved, but accuracy is lost
Solution Approach 1:
The patent applies parameter changes by systematically varying the sparsity levels and quantization bit-widths to find optimal configurations. Different sparsity patterns (e.g., 50%, 75%, 90% sparsity) and quantization precisions (e.g., 8-bit, 4-bit, 2-bit) are tested to achieve the best balance between model compression and accuracy retention for each layer
Solution Approach 2:
The patent implements local quality by applying different sparsification and quantization strategies to different layers of the neural network. Important layers maintain higher precision while less critical layers undergo more aggressive compression. The Hessian-based importance scoring identifies which layers warrant better preservation
2Quantity of substance
If Hessian and gradient-based methods are used to selectively remove weight values, then compression is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing Hessian matrices and gradient norms before the actual sparsification process. These pre-computed metrics are used to identify which weight values are most important to preserve, allowing the optimization to proceed more efficiently without repeated complex calculations
Solution Approach 2:
The patent uses copying by creating simplified proxy models or surrogate functions that approximate the complex Hessian-based optimization. These copies allow for faster iteration and selection of sparsity patterns without requiring full computation of the expensive Hessian matrices at each step
Data Source
AI summary
Embodiments of the present disclosure include systems and methods for optimizing deep neural network models based on sparsification and quantization. A device may identify a layer in a plurality of layers included in a neural network model, each layer in the plurality of layers comprising a plurality of weight values. The device may select a weight value from the plurality of weight values in the layer. The device may remove the weight value from the plurality of weight values in the layer to produce a modified version of the layer. The device may update remaining weight values in the plurality of weight values in the modified version of the layer, wherein removing the weight value and updating the remaining weight values provides greater compression of the neural network model and reduces loss of accuracy of the neural network model.