DNN Tensor Quantization With Adaptive Codebook Sizing
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Solution Overview
Problem
Existing data compression methods for Deep Neural Networks (DNNs) face challenges in achieving high compression efficiency, particularly due to the large number of parameters and the resulting high inference complexity, which is problematic for devices with limited resources.
Innovation Solution
The proposed method involves encoding and decoding tensor parameters of DNN layers using a codebook-based quantization approach, where the codebook size is determined based on distortion values, and employing pdf-based initialization to minimize entropy and optimize quantization, allowing for efficient compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If codebook-based quantization is used to compress DNN parameters, then compression efficiency is improved, but computational complexity increases due to the need to determine optimal codebook sizes and perform pdf-based initialization
Solution Approach 1:
The patent performs pdf-based initialization and determines optimal codebook sizes in advance during the encoding process. By pre-computing these parameters before actual quantization, the method reduces real-time computational burden while maintaining high compression efficiency. The initialization step establishes probability density functions that guide subsequent quantization operations.
Solution Approach 2:
The patent dynamically adjusts codebook sizes based on distortion values and entropy considerations. Rather than using fixed codebook sizes, the system adapts the codebook dimensions to match the specific characteristics of each tensor being compressed, optimizing the balance between compression ratio and reconstruction quality for different DNN layers and parameters.
2Manufacturing precision
If larger codebook sizes are used to reduce quantization distortion, then manufacturing precision is improved, but device complexity increases due to larger storage requirements
Solution Approach 1:
The patent changes the parameter of codebook size dynamically based on distortion requirements. By adjusting the codebook dimensions according to the specific precision needs of different DNN parameters and layers, the system achieves high quantization precision where needed while using smaller codebooks for less critical parameters, thereby optimizing storage utilization.
Solution Approach 2:
Different codebook sizes are applied to different DNN parameters and layers based on their individual importance and distortion characteristics. Critical parameters receive larger codebooks for higher precision, while less important parameters use smaller codebooks, creating a non-uniform precision distribution that optimizes overall system performance and storage efficiency.
3Productivity
If adaptive codebook size adjustment is implemented to optimize compression, then productivity is improved, but ease of operation deteriorates due to the complexity of managing multiple codebook configurations
Solution Approach 1:
The system performs self-configuration by automatically determining optimal codebook sizes based on distortion values and entropy measurements of the input data. The pdf-based initialization process autonomously establishes appropriate quantization parameters without requiring manual intervention or complex configuration management, enabling the system to adapt to different DNN architectures and data characteristics automatically.
Solution Approach 2:
The patent employs feedback mechanisms where distortion values computed during quantization are used to adjust codebook sizes for subsequent processing. This closed-loop approach continuously optimizes compression efficiency by using reconstruction error measurements to guide codebook configuration decisions, improving productivity while keeping the system self-managing.
Data Source
AI summary
The disclosure relates to a method comprising quantizing parameters of an input tensor, said quantizing using a codebook whose size is obtained according to a distortion value determined between the at least one tensor and a quantized version of said at least one tensor. The disclosure also relates to a method for quantizing parameters of the input tensor using a pdf-based initialization bounded according to at least one first pdf factor, said first pdf factor being selected among several candidate bounding pdf factors according to resulting entropy. The disclosure also relates to corresponding signal; bitstream, storage media and encoder and/or decoder devices.