Deep Neural Network Model Encoding and Decoding via Quantization
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Solution Overview
Problem
Deep neural networks face increased computational complexity as they grow in size and complexity, necessitating efficient methods for encoding and decoding to facilitate their application in various industrial uses.
Innovation Solution
The method involves performing global and local quantization, along with entropy encoding and decoding of quantization information, using techniques such as Exp_Golomb binarization, CABAC, and CAVLC, to efficiently compress and transmit deep neural network models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If global quantization is applied to multiple layers, then model size is reduced, but decoding complexity increases due to additional quantization information processing
Solution Approach 1:
The patent segments quantization information processing by dividing layers into groups and processing each group separately. This allows the system to handle quantization information in manageable portions rather than all at once, reducing peak decoding complexity while still achieving overall model compression through global quantization across multiple layers.
Solution Approach 2:
The patent performs preliminary organization and categorization of quantization information before actual decoding. By pre-processing and structuring the quantization data (including global quantization parameters, layer groupings, and metadata), the system reduces the computational burden during the actual decoding phase, enabling efficient handling of compressed model data.
2Measurement precision
If local quantization is applied to multiple layers, then model precision is improved, but encoding time increases due to detailed quantization processing
Solution Approach 1:
The patent divides layers into groups and applies local quantization processing to each group separately. This segmentation allows the system to maintain high precision through detailed local quantization while managing encoding time by processing groups in parallel or in an optimized sequence, rather than processing all layers sequentially.
Solution Approach 2:
The patent applies different quantization strategies to different local regions (layers/groups) based on their specific characteristics. By identifying which layers benefit most from local quantization and applying it selectively, the system achieves improved precision where needed while avoiding unnecessary processing time for layers where global quantization suffices.
3Adaptability or versatility
If multiple quantization modes are supported, then model adaptability is improved, but device complexity increases due to mode selection and processing
Solution Approach 1:
The patent implements dynamic mode selection where the quantization mode (global, local, or hybrid) is chosen based on the specific characteristics of each layer group and the desired trade-off between compression and precision. This dynamic approach allows the system to adapt to different scenarios without requiring complex fixed-structure processing for all possible modes simultaneously.
Solution Approach 2:
The patent designs a unified quantization framework that can handle multiple quantization modes (global, local, and hybrid) through a single versatile processing architecture. By creating a multi-functional system that can operate in different modes as needed, the patent avoids the complexity of implementing separate dedicated systems for each mode, achieving adaptability through a single flexible structure.
4Productivity
If entropy encoding is applied to quantization information, then transmission efficiency is improved, but decoding complexity increases due to entropy decoding requirements
Solution Approach 1:
The patent performs preliminary organization and structuring of quantization information before entropy encoding. By pre-processing the data to create regular patterns and reduce entropy, the system achieves better compression ratios while reducing the complexity of the subsequent entropy decoding process. The preliminary structuring makes the data more amenable to efficient decoding.
Solution Approach 2:
The patent adjusts entropy encoding parameters and methods based on the specific characteristics of the quantization information being encoded. By dynamically selecting appropriate entropy coding schemes and parameters tailored to the data distribution and patterns, the system achieves high transmission efficiency without requiring overly complex decoding algorithms, balancing compression performance with decoding simplicity.
Data Source
AI summary
Disclosed herein are a method and apparatus for encoding/decoding a deep neural network. According to the present disclosure, the method for decoding a deep neural network may include: in a plurality of layers of the deep neural network, entropy decoding quantization information for a current layer; performing dequantization on the current layer; and obtaining a plurality of layers of the deep neural network. At least one of global quantization and local quantization is performed on the current layer.