Neural Network Weight Compression 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 used 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
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 applies quantization to change the parameter representation from high-precision floating-point numbers to lower-precision discrete values. By transforming the parameter format (e.g., from 32-bit float to 8-bit integer), the patent reduces memory storage requirements and computational complexity while maintaining acceptable prediction accuracy through optimized quantization schemes that preserve critical information.
Solution Approach 2:
The patent extracts and removes redundant or less important parameters from the neural network through pruning techniques. By identifying and eliminating parameters that contribute minimally to prediction accuracy, the patent reduces the overall parameter size and computational burden while retaining the essential functionality of the network.
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 changing the parameter precision through quantization, the patent reduces the computational complexity of inference operations. Lower-precision arithmetic operations require fewer computational cycles and can be executed more efficiently on various hardware platforms, thereby reducing inference runtime while maintaining adequate accuracy.
Solution Approach 2:
The patent segments the parameter space into different precision levels, applying higher precision only where critical for accuracy and lower precision where tolerable. This selective precision approach allows the network to maintain high accuracy in important computations while using reduced precision elsewhere, optimizing the trade-off between accuracy and speed.
3Quantity of substance
If compression is increased to reduce memory complexity, then memory efficiency is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent employs sophisticated quantization strategies that adaptively change parameter representation based on their importance and distribution. By dynamically adjusting the quantization precision for different parameters or network layers, the patent achieves higher compression ratios while preserving prediction accuracy for critical parameters and accepting lower precision for less important ones.
Solution Approach 2:
The patent applies different compression levels to different parts of the neural network based on their local importance. By identifying which parameters or layers contribute most to prediction accuracy and applying higher precision only to those regions, the patent achieves efficient compression overall while maintaining accuracy where it matters most.
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.