Neural Network Quantization via Layer-Dependent Shift Constants
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep neural networks are computation-intensive and memory-consuming, making it difficult to deploy them in resource-constrained settings due to high resource requirements, limiting their use in real-world tasks on low-resource devices.
Innovation Solution
A method and system for quantizing neural networks by determining a shift constant for each layer based on an input dataset to convert floating-point data to fixed-point data, reducing memory consumption and computation time, while iterating through all layers to achieve minimal accuracy loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are deployed with full precision floating-point parameters, then model accuracy is maintained, but memory consumption and computation time increase significantly
Solution Approach 1:
The patent applies parameter changes by systematically reducing the precision of neural network parameters from standard floating-point (32-bit or 64-bit) to lower precision formats (8-bit, 16-bit, or even 4-bit integers). This transformation changes the fundamental parameter representation, enabling significant memory reduction while maintaining acceptable accuracy through techniques like quantization-aware training and calibration on representative datasets
Solution Approach 2:
The patent creates simplified copies of the original high-precision neural network by generating quantized versions that replicate the network's functionality using reduced precision. Multiple quantized copies can be created for different precision levels, allowing deployment on various hardware platforms without modifying the original high-precision model
2Measurement precision
If deep neural networks are deployed with full precision floating-point parameters, then model accuracy is maintained, but computation time increases
Solution Approach 1:
By changing the parameter precision from floating-point to integer representations, the patent enables faster computation operations. Integer arithmetic operations are inherently faster than floating-point operations on most hardware platforms, and the quantization process optimizes parameters for efficient computation while preserving the network's predictive accuracy
Solution Approach 2:
The patent substitutes the mechanical computation system from floating-point arithmetic to integer arithmetic. This substitution leverages the fact that integer operations can be executed more efficiently by hardware, reducing computation time while maintaining the essential computational functionality of the neural network
3Quantity of substance
If quantization is applied to reduce parameter precision, then memory consumption and computation time are reduced, but model accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms through calibration processes where the quantized model is evaluated on a validation dataset, and the quantization parameters (such as scale and zero-point values) are adjusted based on the observed accuracy degradation. This iterative feedback loop continues until the optimal balance between precision reduction and accuracy maintenance is achieved
Solution Approach 2:
The patent applies preliminary action by performing quantization-aware training or calibration before final deployment. During this preliminary phase, the model is trained or adjusted with knowledge of the target quantization format, allowing it to adapt its parameters and representations to the lower precision regime, thereby minimizing accuracy loss when deployed in the quantized state
4Quantity of substance
If standard quantization methods are used, then parameter precision is reduced, but accuracy loss is significant
Solution Approach 1:
The patent applies local quality by using different quantization strategies for different parts of the neural network. Instead of uniformly quantizing all parameters, the method identifies and applies higher precision to critical parameters (such as weights in important layers or bias terms) while using lower precision for less sensitive parameters, thereby maintaining overall accuracy while achieving compression
Data Source
AI summary
The present disclosure relates to a system and method of performing quantization of a neural network having multiple layers. The method comprises receiving a floating-point dataset as input dataset and determining a first shift constant for first layer of the neural network based on the input dataset. The method also comprises performing quantization for the first layer using the determined shift constant of the first layer. The method further comprises determining a next shift constant for next layer of the neural network based on output of a layer previous to the next layer, and performing quantization for the next layer using the determined next shift constant. The method further comprises iterating the steps of determining shift constant and performing quantization for all layers of the neural network to generate fixed point dataset as output.