Neural Network Parameter Coding with Adaptive Quantization Layers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural networks require significant computational resources due to their large number of parameters, necessitating efficient compression and transmission methods, especially in federated learning environments.
Innovation Solution
The use of non-constant quantizers with varying reconstruction levels based on quantization indices to encode and decode neural network parameters, allowing for efficient compression and transmission through reconstruction layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks use a large number of parameters to maintain high representation quality, then the neural network accuracy and performance are improved, but the computational resources, storage requirements, and data transmission bandwidth are significantly increased
Solution Approach 1:
The patent segments the neural network parameters into multiple reconstruction layers, where each layer represents a different level of precision or importance. This allows the system to selectively transmit and process parameters at different quality levels, reducing the total number of parameters that need to be handled at once while maintaining overall representation quality.
Solution Approach 2:
The patent applies different quantization strategies to different portions of the parameter space based on their importance or characteristics. Critical parameters are represented with higher precision while less critical parameters use coarser quantization, optimizing the balance between representation quality and parameter quantity.
2Measurement precision
If neural networks use a large number of parameters to maintain high representation quality, then the neural network accuracy and performance are improved, but the computational resources required for execution are significantly increased
Solution Approach 1:
By dividing parameters into reconstruction layers, the computational workload is distributed across multiple processing stages rather than requiring all parameters to be processed simultaneously. This reduces the peak computational resource requirements while maintaining the ability to achieve high representation quality through progressive refinement.
3Measurement precision
If neural networks use a large number of parameters, then the representation quality is improved, but the data transmission bandwidth and storage requirements are significantly increased
Solution Approach 1:
The parameter set is divided into reconstruction layers that can be transmitted in stages or selectively. This segmentation allows the system to transmit only the necessary parameters for a given quality level, reducing bandwidth consumption while maintaining the option to transmit additional parameters for higher quality if needed.
Solution Approach 2:
The patent extracts and transmits only the essential parameters needed to achieve a target representation quality, rather than transmitting all parameters. Less critical parameters are either omitted or coarsely quantized, reducing transmission bandwidth while maintaining adequate representation quality.
4Quantity of substance
If non-constant quantizers with varying reconstruction levels are used to compress neural network parameters, then the compression efficiency and data transmission are improved, but the encoding and decoding complexity is increased
Solution Approach 1:
The complex quantization process is segmented into multiple reconstruction layers, each with its own quantization strategy. This divides the overall complexity into manageable chunks that can be processed independently, reducing the instantaneous computational burden while achieving efficient compression through the cumulative effect of multiple layers.
Data Source
AI summary
Embodiments according to a first aspect of the present invention are based on the idea, that neural network parameters may be compressed more efficiently by using a non-constant quantizer, but varying same during coding the neural network parameters, namely by selecting a set of reconstruction levels depending on quantization indices decoded from, or respectively encoded, into the data stream for previous or respectively previously encoded neural network parameters. Embodiments according to a second aspect of the present invention are based on the idea that a more efficient neural network coding may be achieved when done in stages—called reconstruction layers to distinguish them from the layered composition of the neural network in neural layers—and if the parametrizations provided in these stages are then, neural network parameter-wise combined to yield a neural network parametrization improved compared to any of the stages.