Neural Network Layer Decomposition Rank Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for decomposing layers in AI models, such as Low Rank Decomposition (LRD), face challenges in selecting the optimal rank for compression, which affects accuracy and compression ratio, as current techniques either prioritize accuracy over compression or vice versa, without considering processing time effectively.
Innovation Solution
A method that calculates the optimal rank of decomposition by maximizing a performance function that balances accuracy, compression ratio, and processing time, using functions like f(r) = rt(r) to determine the rank that minimizes processing time while achieving a desired compression ratio, tailored to specific processors like the Huawei Ascend 910 or V100 GPU.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the rank r is chosen to be small to achieve high compression ratio, then the model size is reduced, but the accuracy drops significantly
Solution Approach 1:
The patent changes the parameter selection approach by using a performance function that evaluates multiple parameters (accuracy, compression ratio, processing time) simultaneously. Instead of fixing the rank parameter arbitrarily, the system dynamically determines the optimal rank by maximizing a composite performance function that balances multiple competing requirements.
2Measurement precision
If the rank r is chosen to be large to preserve accuracy, then the model accuracy is maintained, but the compression ratio is insufficient
Solution Approach 1:
The patent transforms the static rank selection into a dynamic optimization process. The performance function incorporates both accuracy metrics and compression ratio metrics, allowing the system to automatically adjust the effective rank parameter to achieve the desired balance between these two opposing requirements.
3Quantity of substance
If the rank of decomposition is optimized for compression, then the compression ratio improves, but the processing time increases
Solution Approach 1:
The patent introduces processing time as a explicit parameter in the performance function. This allows the optimization process to consider the computational cost of decomposition alongside the compression benefits, automatically finding a balance point where the processing time overhead is acceptable relative to the compression achieved.
4Device complexity
If a fixed rank is used for all layers, then the decomposition process is simple, but the overall model performance is suboptimal
Solution Approach 1:
The patent applies different decomposition ranks to different layers of the neural network based on their individual characteristics and contributions to overall performance. Each layer can have its own optimized rank determined by the performance function, allowing critical layers to maintain higher ranks for accuracy while less critical layers use lower ranks for compression.
Data Source
AI summary
There is described a method for decomposing a layer in an artificial intelligence (AI) model. A rank of decomposition is calculated based on a performance function of a processor. The layer is decomposed into a plurality of matrices based on the rank of decomposition. The layer is replaced in the AI model with the plurality of matrices to produce a compressed AI model.