Neural Network Low-Rank Decomposition for Dataset-Specific Compression
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Solution Overview
Problem
Deep neural networks require significant computing resources due to their large number of layers and parameters, making them difficult to use on devices with limited resources such as mobile phones and edge devices in the IoT context.
Innovation Solution
Implement dataset-dependent low rank decomposition techniques that analyze the relevance of filters and channels in neural networks based on specific datasets, using methods like Tucker and CP Decomposition, to compress the models while retaining effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of layers and parameters of a deep neural network is increased to handle larger datasets, then the model's accuracy and performance are improved, but the computing resources and model size increase significantly
Solution Approach 1:
The patent segments the neural network model into different components (filters, channels, layers) and applies low-rank decomposition to each component independently. This allows the model to be compressed in a structured manner while preserving the functional integrity of each segment, resolving the contradiction between model accuracy and size.
Solution Approach 2:
The patent changes the parameter representation of the neural network by applying low-rank decomposition to transform the full-rank weight matrices into low-rank approximations. This parameter transformation significantly reduces model size while maintaining accuracy through the mathematical property that low-rank decompositions can approximate original matrices with minimal information loss.
2Measurement precision
If the number of layers and parameters of a deep neural network is increased to handle larger datasets, then the model's accuracy and performance are improved, but the computing resources required increase significantly
Solution Approach 1:
By segmenting the computational workload into decomposed low-rank components, the patent enables more efficient computation. The segmented structure allows for optimized matrix multiplications and reduced computational complexity in each segment, thereby reducing overall energy consumption while maintaining model performance.
Solution Approach 2:
The parameter changes through low-rank decomposition fundamentally alter the computational requirements. Instead of computing with full-rank matrices that require O(n²) operations, the low-rank representation enables O(nk) operations where k << n, significantly reducing the computing resources and energy required for inference and training.
3Device complexity
If dataset-dependent low rank decomposition is applied to compress the neural network model, then the model size and computational requirements are reduced, but the model accuracy may be affected
Solution Approach 1:
The patent performs preliminary actions by training the neural network on the target dataset before applying low-rank decomposition. This preliminary training ensures that the model has learned dataset-specific patterns, and the subsequent decomposition preserves these patterns more effectively, maintaining accuracy while achieving compression.
Solution Approach 2:
The patent incorporates feedback mechanisms where the decomposition process is guided by dataset-dependent statistics and performance metrics. The decomposition ranks and truncations are adjusted based on feedback from validation performance, ensuring that accuracy is maintained while achieving the desired compression level.
Data Source
AI summary
Methods, systems, and computer program products for dataset dependent low rank decomposition of neural networks are provided herein. A computer-implemented method includes obtaining a target dataset and a trained model of a neural network; providing at least a portion of the target dataset to the trained model; determining relevance of each of one or more of filters of the neural network and channels of the neural network to the target dataset based on the provided portion, wherein the one or more of the filters and the channels correspond to at least one layer of the neural network; and compressing the trained model of the neural network based at least in part on the determined relevancies.