Deep Neural Network Weight Encoding via Clustering Quantization
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Solution Overview
Problem
Deep Neural Networks (DNNs) face challenges in efficient transmission and storage due to their massive number of parameters, leading to high computational costs, which is problematic for devices with limited resources.
Innovation Solution
A method involving encoding and decoding of DNN weights using a codebook obtained through clustering, considering the probability of data appearance and impact on network accuracy, with quantization and entropy coding techniques, and utilizing a gradient-based importance metric for efficient compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DNN parameters are transmitted and stored in full precision, then model accuracy is maintained, but storage space and transmission bandwidth are excessively consumed
Solution Approach 1:
The patent applies parameter changes by transforming the precision representation of DNN parameters from full precision to quantized low-precision representation. The system divides the parameter range into discrete levels (e.g., 8 levels for 3-bit representation) and maps original parameters to these quantized levels, significantly reducing storage requirements while maintaining acceptable model accuracy through intelligent quantization strategies.
Solution Approach 2:
The patent implements local quality by applying different quantization strategies to different parts of the neural network based on their importance. The system calculates importance metrics for different parameter groups and applies finer quantization to critical parameters while using coarser quantization for less important ones, thereby optimizing the balance between compression ratio and model performance.
2Quantity of substance
If DNN parameters are compressed using traditional methods, then storage space is reduced, but model accuracy deteriorates significantly
Solution Approach 1:
The patent applies preliminary action by performing importance assessment and parameter grouping before the actual quantization process. The system calculates importance metrics, clusters parameters with similar characteristics, and determines optimal quantization levels in advance, which guides the subsequent compression process to preserve critical information while achieving high compression ratios.
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates model performance after quantization and uses this information to adjust quantization parameters. The importance metric calculation and accuracy evaluation form a feedback loop that enables iterative optimization of the quantization strategy to maintain model accuracy while maximizing compression.
3Measurement precision
If quantization levels are increased to maintain accuracy, then model precision is improved, but compression ratio decreases
Solution Approach 1:
The patent applies local quality by assigning different quantization precisions to different parameter groups based on their importance metrics. Critical parameters receive higher precision (more quantization levels) while less important parameters use lower precision, optimizing the overall compression ratio without significantly compromising model accuracy.
Solution Approach 2:
The patent dynamically adjusts quantization parameters based on the distribution characteristics of different parameter groups. The system analyzes parameter distributions and adapts the number of quantization levels accordingly, using fewer levels for parameters with concentrated distributions and more levels for parameters requiring higher precision representation.
Data Source
AI summary
The present disclosure relates to a method including encoding a data set in a signal, the encoding comprising quantizing the data set by using a codebook obtained by clustering the data set, the clustering taking account of a probability of appearance of data in the dataset; the probability being bounded to a bounding value. The present disclosure also relates to a method including encoding in a signal a first weight of a layer of a Deep Neural Network, the encoding taking into account an impact of a modification of a second weight on an accuracy of the Deep Neural Network. The present disclosure further relates to the corresponding signal, decoding methods, devices, and computer readable storage media