Neural Network Weight Encoding With Context-Adaptive 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 employed for encoding and decoding neural network weight parameters, 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 size is 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. Sensitive parameters that significantly affect prediction accuracy are encoded with higher precision (e.g., 16-bit or 32-bit), while less sensitive parameters use lower precision (e.g., 8-bit or 4-bit). This selective approach maintains overall prediction accuracy while reducing total memory usage and inference runtime.
Solution Approach 2:
The patent dynamically adjusts encoding precision parameters based on parameter sensitivity analysis. By changing the bit-depth parameter for different weight and activation parameters, the system optimizes the balance between compression ratio and prediction accuracy, resolving the contradiction between reduced inference runtime and maintained accuracy.
2Ease of manufacture
If uniform quantization is applied to all parameters, then encoding simplicity is improved, but compression efficiency and accuracy deteriorate
Solution Approach 1:
Instead of uniform quantization, the patent implements differential quantization where each parameter or parameter group is quantized according to its specific characteristics and sensitivity. This approach maintains encoding relative simplicity while significantly improving compression efficiency by allocating bits based on actual information content rather than uniformly across all parameters.
Solution Approach 2:
The patent introduces dynamic quantization parameters that adapt to the statistical properties and sensitivity of different parameters. The encoding precision is dynamically adjusted based on parameter importance, allowing the system to achieve better compression efficiency without substantially increasing encoding complexity.
3Measurement precision
If high precision encoding is used for all parameters, then prediction accuracy is maintained, but memory complexity and computational overhead increase
Solution Approach 1:
The patent identifies and separates parameters into different categories based on their sensitivity to quantization errors. Only parameters where high precision is critical for maintaining prediction accuracy are encoded with high precision, while other parameters use lower precision encoding. This selective approach maintains necessary prediction accuracy while reducing overall memory complexity.
Solution Approach 2:
The patent extracts and identifies sensitive parameters through sensitivity analysis, then applies specialized high-precision encoding only to these extracted parameters. The remaining non-sensitive parameters are encoded with standard lower precision, reducing memory complexity while preserving prediction accuracy for the critical parameters.
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.