Neural Network Tensor Decomposition for Memory-Limited Deployment
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Solution Overview
Problem
Deep Neural Networks (DNNs) face challenges in deployment to resource-constrained environments due to high storage and computational demands, with existing compression methods like sparsification and quantization offering limited solutions, especially for convolutional neural networks (CNNs), which often result in accuracy losses.
Innovation Solution
A systematic framework for tensor decomposition-based model compression using the Alternating Direction Method of Multipliers (ADMM) is proposed, formulating tensor decomposition as an optimization problem with constraints on tensor ranks to iteratively reduce model size while maintaining high accuracy, applicable to both CNNs and recurrent neural networks (RNNs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If tensor decomposition-based compression is applied to reduce model size, then storage requirements and computational demands are reduced, but model accuracy may be degraded
Solution Approach 1:
The patent changes the parameter of tensor rank from fixed to variable, allowing the system to adaptively select optimal rank values during compression. This enables finding the best balance between compression ratio and accuracy by adjusting the rank parameter, rather than using a predetermined fixed rank that may either over-compress or under-compress the model.
Solution Approach 2:
The patent introduces dynamic adaptability into the compression process by allowing different layers and tensors to have different rank values. The system dynamically determines optimal ranks based on the specific characteristics of each tensor, enabling flexible compression that maintains accuracy where needed while achieving high compression where possible.
2Ease of operation
If fixed rank tensor decomposition is used to simplify the compression process, then ease of operation is improved, but adaptability to different model structures and compression requirements is reduced
Solution Approach 1:
The patent implements dynamic rank assignment that adapts to different tensor characteristics within the same model and across different models. Each tensor can have its rank independently optimized, allowing the system to handle diverse model architectures (CNNs, RNNs, Transformers) and different compression requirements without being constrained by a fixed rank structure.
Solution Approach 2:
The patent applies different rank values to different tensors and layers based on their specific importance and characteristics. Critical layers maintain higher ranks to preserve accuracy, while less critical layers use lower ranks for maximum compression. This local optimization approach enables the system to adapt to different model structures and requirements.
Data Source
AI summary
Systems and methods provide improved neural network compression by training, based on training data and an optimization problem, a deep neural network to produce a trained deep neural network by iteratively updating a weight matrix of the deep neural network according to, at each iteration, minimizing a rank value of the weight matrix until a memory capacity metric is satisfied, minimizing a loss function based on the training data and the weight matrix and updating the weight matrix, and terminating the iterations upon the loss function being minimized within the memory capacity metric. Tensor decomposition is used to compress the trained deep neural network based on the rank value and the weight matrix to obtain a trained tensor decomposition format deep neural network. The trained tensor decomposition format deep neural network is retrained with the training data to obtain a fine-tuned trained tensor decomposition format deep neural network.