Neural Network Weight Compression With Context-Dependent Arithmetic Coding
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Solution Overview
Problem
Existing neural networks require significant computational resources for inference due to their large parameter sizes, and existing relevance estimation methods do not effectively balance compression and prediction accuracy.
Innovation Solution
A context-dependent arithmetic coding method is used for encoding and decoding neural network weight parameters, selecting contexts based on previously encoded/decoded parameters to optimize compression and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks use large parameter sizes for high prediction accuracy, then prediction accuracy is improved, but memory complexity and computational resources increase
Solution Approach 1:
The patent segments the neural network parameters into different groups (e.g., first group and second group of parameters) with different precision requirements. Less important parameters are represented with lower precision (fewer bits) while critical parameters maintain higher precision, thereby reducing overall parameter size while preserving prediction accuracy.
Solution Approach 2:
Different precision levels are assigned to different parameters based on their local importance to the prediction task. The system identifies which parameters contribute most to prediction accuracy and allocates more bits to those specific parameters, while using fewer bits for parameters with less impact, achieving efficient resource allocation.
2Measurement precision
If neural networks use large parameter sizes for high prediction accuracy, then prediction accuracy is improved, but inference runtime increases
Solution Approach 1:
By segmenting parameters into groups with different precision requirements, the system reduces the total number of computations needed during inference. Lower-precision parameters require fewer computational operations, thereby reducing inference runtime while maintaining overall prediction accuracy through selective high-precision representation of critical parameters.
3Quantity of substance
If compression is applied to neural network parameters, then memory complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The system applies differential compression where different compression levels are applied to different parameters based on their importance. Critical parameters that significantly impact prediction accuracy are compressed less or not at all, while less important parameters are compressed more aggressively, thereby reducing overall parameter size while preserving prediction accuracy.
Solution Approach 2:
Parameters are segmented into different groups based on their importance to prediction accuracy. The first group of parameters is represented with higher precision while the second group uses lower precision, allowing the system to reduce overall parameter size while maintaining the accuracy contributions from the most important parameters.
4Device complexity
If uniform precision is used for all parameters, then device complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
Instead of uniform precision, the system applies local quality differentiation where each parameter group receives precision tailored to its importance. This is managed through context-dependent arithmetic coding that adapts to the specific characteristics of different parameter groups, achieving good prediction accuracy without excessive encoding complexity.
Data Source
AI summary
An encoder for encoding weight parameters of a neural network is configured to obtain a plurality of weight parameters of the neural network, to encode the weight parameters of the neural network using a context-dependent arithmetic coding, to select a context for an encoding of a weight parameter, or for an encoding of a syntax element of a number representation of the weight parameter, in dependence on one or more previously encoded weight parameters and/or in dependence on one or more previously encoded syntax elements of a number representation of one or more weight parameters, and to encode the weight parameter, or a syntax element of the weight parameter, using the selected context. Corresponding decoder, quantizer, methods and computer programs are also described.