Neural Network Quantization With Mixed Bit Depths
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional quantization techniques struggle to efficiently compress neural network models for deployment on hardware with limited resources while maintaining performance.
Innovation Solution
A method that adjusts and quantizes parameters of neural network models using differentiated bit ranges and training processes, specifically adjusting a first set of parameters within a smaller range and a second set within a larger range, followed by quantizing with different bit numbers to improve compression and execution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If conventional quantization technique is used to reduce model size, then the model size is reduced, but the model precision and performance deteriorate
Solution Approach 1:
The patent applies different quantization bit depths to different parts of the neural network model based on their importance and characteristics. Specifically, it uses mixed precision quantization where certain layers or parameters are quantized to lower precision (e.g., INT4) while others maintain higher precision (e.g., INT8 or FP16), thereby achieving local optimization of the precision-compression tradeoff
Solution Approach 2:
The patent changes the quantization parameters (bit depth, range, scale) based on the statistical properties of the model parameters. It dynamically adjusts quantization ranges and scales during training to preserve the distribution characteristics of parameters, thereby maintaining model precision while achieving compression
2Ease of manufacture
If uniform quantization is applied to all parameters, then the quantization process is simple, but the compression efficiency and execution efficiency are suboptimal
Solution Approach 1:
The patent implements differentiated quantization strategies for different parameter types (e.g., weights, biases, activation values) and different network layers. It identifies which parameters benefit most from compression and applies appropriate quantization levels, achieving higher compression efficiency without significantly increasing process complexity
Solution Approach 2:
The patent segments the neural network model into different parts or layers and applies specific quantization schemes to each segment. This allows optimization of compression for each segment based on its characteristics, improving overall compression efficiency while maintaining a manageable quantization process
3Measurement precision
If high precision is maintained for all parameters, then the model performance is preserved, but the model size increases and hardware compatibility decreases
Solution Approach 1:
The patent changes the precision level of parameters based on their sensitivity analysis. It identifies parameters that are less sensitive to quantization errors and reduces their precision, while maintaining high precision for critical parameters, thereby reducing model size while preserving performance
Solution Approach 2:
The patent applies different precision levels to different parts of the model based on local sensitivity and importance. Critical layers or parameters that significantly impact model performance maintain higher precision, while less critical parts are compressed to lower precision, achieving local optimization of the precision-size tradeoff
4Volume of stationary object
If aggressive quantization is applied to achieve high compression, then the model size is significantly reduced, but the execution efficiency and accuracy deteriorate
Solution Approach 1:
The patent optimizes quantization parameters (range, scale, zero-point) specifically for execution efficiency on target hardware. It adjusts these parameters to minimize quantization errors while maintaining compression, and incorporates execution efficiency considerations into the quantization strategy
Solution Approach 2:
The patent uses feedback from model performance evaluation and hardware performance testing to iteratively optimize the quantization scheme. It measures the impact of quantization on accuracy and execution efficiency, and adjusts the quantization strategy accordingly to achieve the desired balance between compression and performance
Data Source
AI summary
A method comprises updating a neural network model based on a training dataset; adjusting a first set of parameters of a first part of the updated neural network model to being within a first range, and adjusting a second set of parameters of a second part of the updated neural network model to being within a second range, a size of the second range exceeding that of the first range. The method further comprises quantizing the adjusted first set of parameters with a first number of bits and the adjusted second set of parameters with a second number of bits, the second number being greater than the first number. In this way, the parameters are quantified in differentiated manners in combination with a training process, and the compression efficiency and execution efficiency of the neural network model may be improved while maintaining the parameter precision and model performance.