Neural Network Parameter Serialization for Quantized Model Compression
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Solution Overview
Problem
The high data requirements for neural networks, particularly due to large parameter tensors, lead to inefficiencies in transmission and updating, as well as increased computational complexity, which hampers their applicability in certain scenarios.
Innovation Solution
Encoding neural network parameters using quantization indices and providing reconstruction rules for each portion of the network, allowing for efficient encoding, decoding, and transmission by utilizing serialization parameters, numerical computation representation parameters, layer type parameters, and other structural parameters to optimize data representation and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network parameters are stored using standard floating-point types in container formats, then the precision and quality of neural network representation is maintained, but the coded size of the representation becomes very large, leading to transmission and storage inefficiency
Solution Approach 1:
The patent applies parameter changes by transforming neural network parameters from standard floating-point representation to quantized integer representation. This involves changing the numerical format and precision level of the parameters, allowing them to be stored more efficiently while maintaining acceptable reconstruction quality through dequantization processes.
Solution Approach 2:
The patent segments the neural network parameters into different groups or layers, applying different quantization strategies to different segments. This allows selective optimization where critical parameters maintain higher precision while less critical ones use coarser quantization, balancing overall representation quality with compression efficiency.
2Productivity
If frequent transmission and updates of neural networks are performed, then the model can be updated and improved, but the data rate required becomes a serious bottleneck
Solution Approach 1:
By changing the parameter representation from high-precision floating-point to compressed quantized formats, the patent reduces the data volume that needs to be transmitted during frequent updates, directly addressing the data rate bottleneck while enabling more frequent model iterations.
3Quantity of substance
If the coded size of neural network representations is reduced through compression, then transmission efficiency improves, but the complexity of encoding and decoding processes increases
Solution Approach 1:
The patent manages encoding/decoding complexity by systematically changing parameters from floating-point to quantized formats using defined transformation rules. The complexity is controlled through structured quantization schemes and efficient dequantization algorithms that balance compression ratios with computational overhead.
Data Source
AI summary
Data stream having a representation of a neural network encoded thereinto, the data stream including serialization parameter indicating a coding order at which neural network parameters, which define neuron interconnections of the neural network, are encoded into the data stream.