Vector Quantized Neural Model Parameter Segmentation
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Solution Overview
Problem
Deep neural networks have a high number of parameters, making them computationally demanding and resource-intensive to train and deploy, while also exhibiting parameter redundancy that affects efficiency and interpretability.
Innovation Solution
The method involves vector quantization by dividing the parameters of each layer into non-overlapping segments, creating a codebook with representative vectors, and updating the neural model with this codebook, which reduces the overall number of parameters and promotes resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks use a vast number of parameters to achieve high performance, then model accuracy and capability are improved, but computational resource consumption and training time increase significantly
Solution Approach 1:
The patent segments the neural network parameters into codebooks, where each codebook contains a limited set of representative vectors. Instead of using all original parameters, the model segments parameter usage into discrete codebook lookups, reducing the effective parameter count while maintaining representational capacity through the segmented structure.
Solution Approach 2:
The patent creates codebooks as compressed representations (copies) of the original parameter sets. These codebooks serve as simplified copies that capture the essential information needed for model operation, allowing the system to use fewer parameters while maintaining performance through the copied representations.
2Reliability
If deep neural networks include millions to billions of parameters, then model capability is enhanced, but device complexity and deployment difficulty increase
Solution Approach 1:
The patent divides the large parameter space into multiple codebooks, each containing a manageable number of representative vectors. This segmentation transforms the complexity from handling billions of individual parameters to managing fewer codebook structures, making the model more deployable on resource-constrained devices.
Solution Approach 2:
The patent fundamentally changes the parameter representation from continuous high-dimensional values to discrete codebook indices. This parameter transformation reduces the effective complexity by replacing millions of parameters with a much smaller set of codebook references, while the codebooks themselves are optimized to maintain model capability.
3Measurement precision
If the neural model uses many parameters, then model accuracy is improved, but inference speed and resource efficiency decrease
Solution Approach 1:
The patent creates codebooks as compressed copies of parameter sets that can be rapidly accessed during inference. These codebook copies enable fast lookups compared to processing full parameter sets, improving inference speed while the codebook content is designed to preserve model accuracy through representative vector selection.
Data Source
AI summary
In some implementations, the device may include receiving a training dataset. In addition, the device may include training a neural model with the training dataset to generate a first layer having weighted parameters. The device may include dividing the first layer into a first predetermined number of segments based on the first layer being a first type of layer. Moreover, the device may include generating a codebook by replacing the weighted parameters in each segment of the first layer with a codeword based on finding a representative vector which most closely relates to the weighted parameters of each segment in a vectorization dictionary, where the codebook includes a number of codewords equal to the first predetermined number of segments and each codeword includes the representative vector. Also, the device may include in response to updating the neural model with the codebook, outputting a trained neural model that includes the codebook which replaces the first layer.