Hierarchical DL Model Optimization for Automated Compression
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Solution Overview
Problem
Conventional deep learning model optimization methods require manual intervention and expert knowledge, are time-consuming, and lack automation, especially in constrained resource environments, leading to inefficiencies and accuracy loss.
Innovation Solution
A two-step hierarchical model optimization approach using Automated Pruning-weight Clustering-Quantization (AutoPCQ) followed by Automated Knowledge Distillation (AKD), employing Bayesian Optimization and Reinforcement Learning to automate configuration selection and student model search, with restricted Neural Network Architecture Search to optimize deep learning models for resource-constrained devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual model optimization methods (pruning, weight clustering, quantization) are used, then model accuracy can be maintained, but the process requires extensive manual intervention and trial-and-error experiments
Solution Approach 1:
The system enables automated model optimization by allowing the optimization framework to automatically perform pruning, weight clustering, and quantization operations without requiring manual configuration. The framework self-adjusts parameters and iteratively optimizes the model based on performance metrics, eliminating the need for data scientists to manually conduct trial-and-error experiments while maintaining model accuracy.
Solution Approach 2:
The optimization framework systematically varies key parameters including pruning ratios, cluster counts, and quantization bit-widths to find the optimal configuration. By automatically adjusting these parameters and evaluating their impact on model accuracy and resource consumption, the system resolves the contradiction between maintaining precision and reducing manual intervention.
2Quantity of substance
If knowledge distillation is used to train a smaller student model, then memory footprint is reduced, but the process requires expert intervention for loss function design and training configuration
Solution Approach 1:
The knowledge distillation process is automated within the optimization framework, which automatically designs the loss function, configures training parameters, and iteratively trains the student model. The framework monitors training progress and adjusts configurations without requiring expert intervention, thereby reducing memory footprint while simplifying the complex configuration process.
Solution Approach 2:
The system implements feedback mechanisms where the optimization framework continuously evaluates student model performance against the teacher model and adjusts training parameters accordingly. This automated feedback loop handles the complexity of loss function design and training configuration, enabling memory reduction through knowledge distillation without requiring expert-level configuration knowledge.
3Quantity of substance
If aggressive pruning and quantization are applied to reduce model size, then resource requirements decrease, but model accuracy deteriorates
Solution Approach 1:
The framework systematically explores different parameter configurations including varying pruning ratios, weight clustering counts, and quantization bit-widths. By automatically adjusting these parameters and evaluating their impact on both model size and accuracy, the system finds the optimal balance point that reduces model size while minimizing accuracy deterioration.
Solution Approach 2:
The optimization process is dynamic and iterative, allowing the system to adaptively adjust optimization aggressiveness based on performance feedback. The framework can dynamically modify pruning and quantization parameters during the optimization process to prevent excessive accuracy loss while achieving significant model size reduction, resolving the contradiction between these two objectives.
4Ease of manufacture
If conventional optimization frameworks (OpenVino, Tensor Flow) are used, then model conversion and optimization functionality is available, but they require significant trial-and-error experiments and expert knowledge
Solution Approach 1:
The optimization framework automatically performs model conversion, optimization, and evaluation without requiring manual trial-and-error experiments. The system self-configures parameters, selects appropriate optimization techniques, and iteratively refines the model based on performance metrics, thereby maintaining ease of manufacture while significantly reducing the time and expert knowledge required compared to conventional frameworks.
Data Source
AI summary
State of art approaches independently use a Pruning-weight Clustering-Quantization (PCQ) or Knowledge Distillation (KD) for model optimization and require critical manual intervention. Embodiments of the present disclosure provide a method and system for the two-step hierarchical model optimization approach for generating optimized model DL model. The method comprises a AutoPCQ technique followed by conditional application of an automated KD (AKD) technique. The AutoPCQ technique formulates a problem of configuration selection of the DL model as an optimization problem by iteratively applying Bayesian optimization and Reinforcement Learning. Further, the AKD technique formulates automated search of a student model as the optimization problem with the DL model representing a teacher model. A search space for the student model is defined by a restricted Neural Network Architecture Search that restricts the search space. The method automates the model optimization, in time efficient manner without compromising accuracy of the optimized model.