Neural Network Weight Decoding With Layer-Adaptive Arithmetic Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural networks require significant computational resources and high transmission rates due to their large number of parameters, making efficient encoding and decoding challenging, especially in terms of balancing compression, complexity, and computational costs.
Innovation Solution
A context-dependent arithmetic coding and decoding method is employed for encoding and decoding neural network weight parameters, using probability estimation parameters to adapt to different neural network layers and context models, allowing for efficient representation and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context-dependent arithmetic coding with probability estimation parameters is used, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting probability estimation parameters based on context models and neural network layer characteristics. Different probability parameters are selected for different bins and contexts, allowing the coding system to adapt to varying statistical properties of weight parameters across different layers and contexts, thereby improving coding efficiency without requiring a complete redesign of the decoding architecture.
Solution Approach 2:
The patent implements dynamics through context-dependent probability estimation where the probability parameters are not fixed but adapt dynamically based on the current context model and layer information. The decoder maintains multiple context models and selectively applies different probability parameters depending on the specific bin being decoded, enabling the system to respond dynamically to varying data characteristics while maintaining a structured decoding framework.
2Loss of substance
If different probability estimation parameter values are used for different neural network parameters and context models, then compression efficiency is improved, but computational costs increase
Solution Approach 1:
The patent applies segmentation by dividing the neural network parameters into different context models and layers, with each segment having its own optimized probability estimation parameters. This segmentation allows the compression system to treat different parameter groups independently, applying appropriate probability models to each segment based on its statistical characteristics, thereby improving overall compression efficiency while managing computational complexity through localized processing.
Solution Approach 2:
The patent implements local quality by assigning different probability estimation parameter values to different local contexts and neural network layers. Instead of using a uniform probability model for all parameters, the system tailors the probability parameters to the specific local characteristics of each context model and layer, optimizing compression for each local region while maintaining overall system efficiency.
Data Source
AI summary
A decoder for decoding weight parameters of a neural network, wherein the decoder is configured to obtain a plurality of neural network parameters of the neural network on the basis of an encoded bitstream. Furthermore, the decoder is configured to decode the neural network parameters of the neural network using a context-dependent arithmetic decoding Moreover, the decoder is configured to obtain a probability estimate for a decoding of a bin of a number representation of a neural network parameter using one or more probability estimation parameters. In addition, the decoder is configured to use different probability estimation parameter values for a decoding of different neural network parameters and/or to use different probability estimation parameter values for a decoding of bins associated with different context models. Some embodiments are configured to use different probability estimation parameter values for a decoding of neural network parameters of different layers of the neural network.