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 during quantization.
Innovation Solution
A context-dependent arithmetic coding method is employed 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 applies quantization to change the precision parameters of weight parameters from high-precision floating-point representation to low-precision integer representation. This parameter transformation reduces the quantity of storage required 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 weight parameters from the neural network through pruning techniques. By identifying and eliminating parameters that contribute minimally to prediction accuracy, the overall parameter size is reduced while preserving the network's essential functionality and accuracy.
2Quantity of substance
If existing relevance estimation methods are used for quantization, then compression is achieved, but prediction accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where quantization parameters are optimized based on their impact on prediction accuracy. The system continuously monitors accuracy degradation and adjusts quantization strategies accordingly, using gradient-based methods to refine weight parameters after quantization and compensate for information loss.
Solution Approach 2:
The patent employs dynamic quantization schemes where the precision and quantization parameters are adapted based on the importance and distribution of different weight parameters. Rather than applying uniform quantization, the system dynamically adjusts quantization granularity to preserve critical parameters while compressing less important ones, balancing compression and accuracy.
3Ease of manufacture
If uniform quantization is applied to all weight parameters, then implementation is simplified, but compression efficiency and accuracy are reduced
Solution Approach 1:
The patent applies different quantization schemes and precision levels to different regions or types of weight parameters based on their local characteristics and importance. Critical parameters receive higher precision treatment while less important parameters use aggressive compression, optimizing the overall balance between accuracy and compression efficiency.
Solution Approach 2:
The patent segments the weight parameters into different groups or channels based on their importance, distribution characteristics, or functional roles. Each segment is then quantized using optimized parameters tailored to its specific characteristics, improving overall compression efficiency compared to uniform quantization while maintaining implementation feasibility.
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.