Neural Network Layer Quantization for Hardware Efficiency
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Solution Overview
Problem
Current model quantization technologies preset quantization bit widths without considering the actual network model structure and hardware environment, leading to inefficient data processing.
Innovation Solution
A method that marks each layer of a network model as a key or non-key layer based on structural information, determines optimal quantization bit widths for each layer according to hardware resources, and trains the model to obtain an optimal network model for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If preset quantization bit width is used without considering network model structure and hardware environment, then quantization processing can be performed, but data processing efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating layers into key layers and non-key layers based on their importance to model accuracy. Key layers are assigned higher quantization bit widths while non-key layers receive lower bit widths, optimizing the balance between compression and accuracy for each specific layer rather than applying uniform quantization throughout the entire model.
Solution Approach 2:
The patent dynamically adjusts quantization bit width as a parameter based on layer importance classification. By changing the quantization bit width parameter differently for key and non-key layers, the system optimizes data processing efficiency while maintaining model accuracy, directly addressing the contradiction between quantization feasibility and processing efficiency.
2Quantity of substance
If network model size is reduced through quantization, then hardware cost and power consumption decrease, but model accuracy may deteriorate
Solution Approach 1:
The patent preserves model accuracy by applying higher quantization bit widths to key layers that are critical for maintaining accuracy, while allowing lower bit widths for non-key layers. This localized approach ensures that model accuracy is maintained in important regions while still achieving overall model size reduction and efficiency improvements.
Solution Approach 2:
The patent segments the network model layers into key layers and non-key layers based on their importance. This segmentation allows different quantization strategies to be applied to different segments, optimizing the trade-off between model size reduction and accuracy preservation by treating critical and non-critical parts differently.
3Device complexity
If uniform quantization bit width is applied to all layers, then processing is simplified, but optimization for specific hardware environment and model structure is lost
Solution Approach 1:
The patent introduces local quality to handle hardware adaptability by classifying layers into key and non-key categories based on their specific roles in the model. This allows the quantization strategy to be adapted to the specific hardware environment and model structure, improving versatility while maintaining manageable processing complexity through systematic classification.
Solution Approach 2:
The patent performs preliminary classification of layers into key and non-key layers before applying quantization. This preliminary action organizes the complex task of hardware-adaptive quantization into a systematic process, making the complex optimization manageable while ensuring the final quantization strategy is well-adapted to the specific hardware environment and model structure.
Data Source
AI summary
A data processing method, system and device, and a readable storage medium. The method includes: marking each layer of a network model as a key layer or a non-key layer according to acquired structural information of the network model; respectively determining a quantization bit width range of the key layer and a quantization bit width range of the non-key layer according to hardware resource information that needs to be deployed; determining, in the quantization bit width range, optimal quantization bit widths of each layer of the network model; and training the network model based on the optimal quantization bit widths of each layer of the network model, so as to obtain an optimal network model, and performing data processing using the optimal network model. According to the present disclosure, for an optimal network model obtained by means of performing training based on an optimal quantization bit width, insofar as the optimal accuracy of the network model is ensured, the model structure is compressed to the maximum extent, so as to realize the optimal deployment of a hardware end, such that the efficiency of processing data by means of the optimal network model is improved.