Neural Network Weight Compression for Mobile Model Deployment
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Solution Overview
Problem
Large neural network models require significant storage and computational resources, posing challenges for deployment on devices with limited processing power and memory, such as mobile devices, due to their large size and complexity.
Innovation Solution
The implementation of neural network model compression techniques, including quantization and partitioning, allows for the efficient encoding and decoding of neural network models, reducing storage requirements and computational complexity by using bitstreams and syntax elements to reconstruct weight coefficients and partition tensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If neural network models are deployed on resource-constrained devices, then accessibility and usability improve, but storage requirements and computational complexity become prohibitive
Solution Approach 1:
The patent divides the neural network model into multiple blocks, where each block contains a subset of weight coefficients. This segmentation allows selective transmission and processing of only necessary model components, reducing overall storage requirements and enabling deployment on devices with limited memory capacity.
Solution Approach 2:
The patent extracts and transmits only the essential syntax elements and weight coefficients needed for model operation, separating critical information from redundant data. This extraction process reduces the quantity of data that must be stored and processed on resource-constrained devices.
2Measurement precision
If full-precision weight coefficients are used in neural network models, then model accuracy is maintained, but storage space and processing requirements increase significantly
Solution Approach 1:
The patent changes the precision parameter of weight coefficients by transmitting them in a compressed format with associated syntax elements that define their interpretation. This allows the model to maintain adequate precision for accurate inference while significantly reducing the storage space required to hold the weight coefficients.
Solution Approach 2:
The patent transmits weight coefficients with varying levels of precision based on their importance and distribution characteristics. Less critical coefficients are represented with lower precision, while critical coefficients maintain higher precision, achieving a balance between model accuracy and storage efficiency.
3Measurement precision
If complex neural network architectures are used, then performance and recognition accuracy improve, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the complex neural network architecture into manageable blocks with defined syntax elements for each. This segmentation allows the device to process and reconstruct model components incrementally, reducing the peak computational complexity required to operate the full model while maintaining overall recognition accuracy.
4Measurement precision
If comprehensive model parameters are transmitted, then model reconstruction accuracy is ensured, but transmission bandwidth and processing overhead increase
Solution Approach 1:
The patent extracts only the essential syntax elements and weight coefficients needed for model reconstruction, eliminating redundant parameter transmissions. This extraction maintains reconstruction accuracy by preserving critical model information while reducing transmission bandwidth requirements and processing overhead.
Solution Approach 2:
The patent transmits model parameters selectively based on their importance and the specific reconstruction needs. Not all parameters are transmitted with equal detail - only those necessary for accurate reconstruction are included, reducing transmission time while maintaining reconstruction accuracy.
Data Source
AI summary
Aspects of the disclosure provide methods and apparatuses for neural network model compression/decompression. In some examples, an apparatus for neural network model decompression includes receiving circuitry and processing circuitry. The processing circuitry decodes, from a bitstream corresponding to a representation of a neural network, at least a syntax element to be applied to multiple blocks in the neural network. Then, the processing circuitry reconstructs, from the bitstream, weight coefficients in the blocks based on the syntax element.