Neural Network Weight Encoding With Dynamic Context Selection
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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 during quantization.
Innovation Solution
A context-dependent arithmetic coding method is used for encoding and decoding neural network weight parameters, where the context is selected based on previously encoded or 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 significantly
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 that preserves important parameters while compressing less critical ones, thus reducing overall parameter size while maintaining prediction accuracy
Solution Approach 2:
The patent transforms weight parameters from their original floating-point representation into quantized integer representations with varying precision levels. By changing the parameter representation format and applying context-dependent encoding, the patent reduces memory requirements while preserving the essential information needed for accurate predictions
2Quantity of substance
If existing relevance estimation methods are used for quantization, then some compression is achieved, but the balance between compression and prediction accuracy is not effectively optimized
Solution Approach 1:
The patent employs context-dependent arithmetic coding where the encoding context is dynamically selected based on previously encoded parameters and their statistical properties. This feedback mechanism allows the encoder to adapt to the local characteristics of the weight parameters, optimizing the balance between compression ratio and prediction accuracy by adjusting encoding precision based on local relevance patterns
Solution Approach 2:
The patent introduces dynamic context selection in the arithmetic coding process, where encoding parameters and precision levels are adjusted on-the-fly based on the statistical characteristics of local parameter groups. This dynamic adaptation enables optimal trade-off between compression and accuracy for different regions of the neural network weights
3Quantity of substance
If context-dependent arithmetic coding is used for encoding weight parameters, then compression efficiency is improved, but encoding and decoding complexity increases
Solution Approach 1:
The patent divides weight parameters into manageable groups with distinct statistical characteristics, allowing the use of simplified context models for each group rather than requiring complex global context analysis. This segmentation reduces encoding complexity while maintaining compression efficiency through localized context-dependent coding
Solution Approach 2:
The patent transforms continuous weight parameters into discrete quantized values with predefined precision levels, simplifying the context-dependent arithmetic coding process. By changing parameters from continuous to discrete domains and establishing predetermined context categories, the patent reduces computational complexity while preserving compression efficiency
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.