Neural Network Compression Using Dependent Quantization
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Solution Overview
Problem
Current neural network model compression techniques face challenges in efficiently compressing and decompressing large models, particularly in devices with limited processing power and memory, such as mobile devices, due to the complexity and size of deep neural networks.
Innovation Solution
The proposed solution involves techniques for neural network model compression and decompression, including dependent quantization, sublayer processing order, and weight unification-based parameter reduction, which allow for efficient encoding and decoding of neural network models by using flags to indicate quantization methods and unification parameters, enabling denser reconstruction of model parameters and reducing distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used to achieve performance improvements in computer vision and speech recognition, then model accuracy is improved, but model size and computational complexity increase
Solution Approach 1:
The patent applies parameter quantization to transform high-precision model parameters (float32) into lower-precision representations (int8, int4, or binary), thereby reducing model size and computational complexity while maintaining acceptable accuracy levels. This is achieved through dependent quantization methods that adaptively determine precision requirements for different parameter groups
Solution Approach 2:
The patent segments the neural network model into multiple independent parameter groups or layers, allowing different quantization strategies to be applied to different segments. This enables selective compression where critical parameters maintain higher precision while less critical parameters use lower precision, resolving the contradiction between overall accuracy and model complexity
2Measurement precision
If deep neural networks are used to achieve performance improvements, then model accuracy is improved, but storage requirements increase
Solution Approach 1:
The patent reduces storage requirements by changing the parameter representation from high-precision floating-point formats to compact integer or binary formats through dependent quantization. This achieves up to 8x compression ratios while maintaining model functionality, directly addressing the storage vs. accuracy contradiction
Solution Approach 2:
The patent creates compressed representations (copies) of the original model parameters that occupy significantly less storage space. These quantized parameter copies retain sufficient information for accurate inference, eliminating the need to store full-precision parameters
3Adaptability or versatility
If deep neural networks are deployed on resource-constrained devices, then model functionality is maintained, but processing power requirements exceed device capabilities
Solution Approach 1:
The patent transforms model parameters to lower precision formats that can be processed efficiently on resource-constrained devices. Integer and binary arithmetic operations required by quantized models consume significantly less power than floating-point operations, enabling deployment on mobile and embedded devices while maintaining functionality
Solution Approach 2:
The patent divides the model into segments that can be processed in stages, allowing resource-constrained devices to handle computation in manageable chunks. This segmentation enables efficient memory usage and power management while preserving overall model functionality
4Adaptability or versatility
If model compression techniques are applied to reduce storage and computational requirements, then device compatibility is improved, but model accuracy deteriorates
Solution Approach 1:
The patent applies different quantization precision levels to different parameter groups based on their importance and sensitivity. Critical parameters maintain higher precision while less sensitive parameters use lower precision, achieving device compatibility without significant accuracy loss. This local quality approach resolves the contradiction between compression and accuracy
Solution Approach 2:
The patent incorporates feedback mechanisms where quantization parameters are adjusted based on performance metrics. The dependent quantization method uses feedback from reconstruction error or accuracy measurements to adaptively optimize the compression level, ensuring device compatibility while minimizing accuracy deterioration
Data Source
AI summary
Methods and apparatuses of neural network model compression/decompression are described. In some examples, an apparatus of neural network model decompression includes receiving circuitry and processing circuitry. The processing circuitry can be configured to receive a dependent quantization enabling flag from a bitstream of a compressed representation of a neural network. The dependent quantization enabling flag can indicate whether a dependent quantization method is applied to model parameters of the neural network. The model parameters of the neural network can be reconstructed based on the dependent quantization method in response to the dependent quantization enabling flag indicating the dependent quantization method is used for encoding the model parameters of the neural network.