Bayesian Sparsity Selection for Accuracy-Constrained Model Compression
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Solution Overview
Problem
Existing techniques for compressing neural networks require manual specification of a target sparsity ratio, which is computationally intensive and inconvenient due to the dependence on the neural network, target platform, and optimization objective, necessitating retraining for each trial.
Innovation Solution
A Bayesian optimizer is used to determine a first sparsity ratio based on a limit for accuracy loss and select a second sparsity ratio that optimizes a predefined objective function, generating a compressed version of the machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual exploration of sparsity ratios is performed, then the optimal compression ratio can be found, but the computational cost and time consumption increase significantly
Solution Approach 1:
The system performs automatic sparsity ratio selection through Bayesian optimization, eliminating the need for manual trial-and-error exploration. The algorithm autonomously determines optimal sparsity ratios by learning from previous compression results and automatically adjusting parameters, thereby resolving the contradiction between finding optimal compression and avoiding manual computational overhead.
Solution Approach 2:
The system performs preliminary compression tests at different sparsity ratios to build a performance model before final deployment. By pre-evaluating multiple sparsity levels and storing results in a performance model, the system avoids repeated full retraining during manual exploration, significantly reducing computational time while maintaining optimal compression identification.
2Measurement precision
If manual exploration of sparsity ratios is performed, then the optimal compression ratio can be found, but the complexity of the compression process increases
Solution Approach 1:
The Bayesian optimization framework automatically manages the complexity of exploring sparsity ratios by self-adjusting the search strategy based on accumulated performance data. The system handles parameter tuning, performance evaluation, and model updates autonomously, transforming a complex manual process into a streamlined automated workflow that reduces operational complexity.
Solution Approach 2:
The system implements feedback loops where compression performance at each sparsity ratio is measured and fed back into the Bayesian optimizer. This feedback mechanism allows the system to learn from previous results and intelligently guide subsequent exploration, reducing the complexity of manual trial-and-error by providing automated guidance based on accumulated knowledge.
3Measurement precision
If full-precision arithmetic is used in neural networks, then computational accuracy is maintained, but memory consumption and execution time increase
Solution Approach 1:
The system changes the precision parameter of neural network weights and activations from full precision (e.g., 32-bit floating point) to lower precision formats (e.g., 8-bit integers or quantized representations). By systematically exploring different precision levels using Bayesian optimization, the system identifies the optimal precision setting that maintains acceptable computational accuracy while significantly reducing memory consumption and execution time.
4Productivity
If higher sparsity ratios are applied, then model compression is improved, but accuracy loss increases
Solution Approach 1:
The system applies partial pruning strategies where not all weights are removed even at high sparsity ratios. By using techniques like structured pruning or selective weight removal combined with fine-tuning, the system achieves high compression ratios while mitigating accuracy loss through targeted rather than exhaustive weight removal, balancing compression efficiency with model performance.
Data Source
AI summary
One embodiment of a method includes determining, by a Bayesian optimizer, a first sparsity ratio associated with a limit on an accuracy loss caused by compressing the machine learning model. The method further includes selecting, by the Bayesian optimizer, a second sparsity ratio that optimizes a predefined objective function for the machine learning model within a search space bounded by the first sparsity ratio. The method further includes generating a compressed version of the machine learning model having the second sparsity ratio.