Neural Network Weight Compression Using 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
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 weight parameters into multiple groups based on their statistical characteristics (e.g., magnitude ranges, distribution patterns). Each group is encoded using context-dependent arithmetic coding with group-specific contexts, allowing differential compression rates while preserving important parameters with higher fidelity and compressing less critical parameters more aggressively.
Solution Approach 2:
The patent applies local quality by assigning different compression contexts to different parameter groups based on their local statistical properties. Parameters with similar characteristics are grouped together and processed with the same context, ensuring that parameters requiring higher precision maintain it while allowing compression of parameters where lower precision is acceptable.
2Quantity of substance
If existing relevance estimation methods are used for compression, then some compression is achieved, but the balance between compression and prediction accuracy is not effective
Solution Approach 1:
The patent changes the parameter representation by using context-dependent arithmetic coding with multiple contexts that model the statistical distribution of weight parameters. Instead of uniform quantization, the system adapts the encoding parameters based on the local statistical properties of parameter groups, achieving better rate-distortion performance and maintaining prediction accuracy at lower bitrates.
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.