Neural Network Compression With Hardware-Specific Architecture Search
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Solution Overview
Problem
Existing techniques for compressing large language models (LLMs) face challenges such as high computational and memory costs, long training times, and accuracy loss due to manual hyperparameter tuning, especially when deploying LLMs across different hardware implementations.
Innovation Solution
A method involving training student model layers to mimic layers of a trained LLM, generating candidate architectures through constrained optimization, and fine-tuning these architectures on calibration data to create optimized student LLMs for specific hardware, eliminating the need for manual hyperparameter tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If model compression techniques (pruning, quantization, knowledge distillation) are applied to reduce model size, then memory footprint and computational costs are reduced, but model accuracy deteriorates
Solution Approach 1:
The patent applies parameter changes by systematically varying compression parameters (pruning ratios, quantization bits, distillation temperatures) to find optimal settings that balance model size reduction with accuracy preservation. The system adjusts these parameters dynamically based on target device constraints and performance requirements.
Solution Approach 2:
The patent implements dynamic adaptation by allowing the compression strategy to adjust based on feedback from performance evaluation. The system can switch between different compression techniques and parameter settings to optimize the trade-off between model size and accuracy for specific deployment scenarios.
2Adaptability or versatility
If Neural Architecture Search (NAS) is used to find optimized architectures for smaller LLMs, then model effectiveness for specific hardware is improved, but training time and computational resources increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training student models to mimic teacher model behavior before deployment. This knowledge distillation step creates a compressed model that already captures essential patterns, reducing the need for extensive subsequent training and evaluation iterations.
Solution Approach 2:
The patent uses copying by creating student models that replicate the behavior and knowledge of larger teacher models. Through knowledge distillation, the student models copy the essential computational patterns and decision-making logic of the teacher models, achieving similar performance with reduced complexity.
3Productivity
If the search space in NAS is shrunk to speed up execution, then training time is reduced, but the likelihood of finding optimized architecture decreases
Solution Approach 1:
The patent applies local quality by focusing the search on specific architectural components and parameters most critical to performance. Instead of exhaustively searching all possible architectures, the system identifies and optimizes key structural elements that have the greatest impact on model effectiveness for the target hardware.
4Manufacturing precision
If manual hyperparameter tuning is performed for model compression, then model accuracy can be maintained, but the process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The patent implements self-service by enabling the compression system to automatically select and adjust hyperparameters based on performance feedback and device constraints. The system self-optimizes compression parameters without requiring manual intervention, reducing errors and time consumption while maintaining accuracy.
Solution Approach 2:
The patent applies feedback by using performance evaluation results to automatically adjust compression parameters and strategies. The system monitors model performance on target devices and uses this feedback to refine hyperparameter settings, creating a closed-loop optimization process that maintains accuracy while reducing manual tuning requirements.
Data Source
AI summary
At least one of the various embodiments is directed towards a computer-implemented method for generating trained artificial neural networks. The method includes, for each model layer included in a trained model, training one or more student model layers to mimic the model layer, for a first target device included in a plurality of target devices, generating one or more candidate architectures based on a constrained optimization problem and the one or more trained student model layers, training the one or more candidate architectures on a set of calibration data, selecting a first candidate architecture included in the one or more candidate architectures that is associated with a least amount of error, and performing a plurality of fine-turning training operations on the first candidate architecture to generate a first trained student model.