Neural Network Weight Encoding 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 weight parameters of neural networks, where the context is selected based on previously encoded/decoded parameters to optimize compression and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural network parameter sizes are reduced through compression, then memory complexity and inference runtime are reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies different quantization precision levels to different parameters based on their importance. Critical parameters maintain higher precision while less important parameters use lower precision, resolving the contradiction by making the system non-uniform - high precision where needed, low precision where acceptable, thus reducing overall memory complexity while preserving prediction accuracy for key parameters.
Solution Approach 2:
The patent dynamically adjusts quantization parameters and precision levels based on parameter importance scores and performance requirements. By changing the precision parameter selectively for different weight parameters, the system achieves optimized inference runtime while maintaining adequate prediction accuracy through adaptive parameter transformation.
2Measurement precision
If quantization precision is increased to maintain prediction accuracy, then memory complexity increases, but compression efficiency deteriorates
Solution Approach 1:
Different quantization bit depths are assigned to different parameters based on their contribution to prediction accuracy. This local differentiation allows the system to maintain high prediction accuracy for critical parameters while using lower precision for others, thereby achieving better compression ratios without sacrificing overall model performance.
Solution Approach 2:
The patent applies full precision only to the extent necessary for maintaining prediction accuracy, and reduces precision elsewhere. This partial application of high precision avoids the excessive memory complexity that would result from uniformly high precision across all parameters, thus improving compression efficiency while preserving adequate prediction accuracy.
3Device complexity
If uniform quantization is applied to all parameters, then device complexity is reduced, but prediction accuracy deteriorates due to loss of important parameter details
Solution Approach 1:
The patent implements non-uniform quantization where each parameter or parameter group receives customized quantization settings based on its importance and statistical properties. This resolves the contradiction by increasing quantization complexity only where necessary to preserve prediction accuracy, while maintaining simpler quantization for less critical parameters.
Solution Approach 2:
The system dynamically determines quantization parameters such as bit depth, scaling factors, and rounding modes based on parameter-specific characteristics like variance, magnitude, and importance scores. This adaptive parameter transformation maintains prediction accuracy for critical parameters while keeping overall device complexity manageable through automated parameter optimization.
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.