Neural Network Serialization Order for Compact Model Encoding
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Solution Overview
Problem
The high data requirements for neural network (NN) representations, particularly in terms of weights and biases, lead to significant bandwidth constraints during transmission and updates, necessitating more efficient compression methods to reduce the coded size and computational complexity while maintaining inference quality.
Innovation Solution
A data stream representation of neural networks that includes serialization parameters indicating the coding order of neuron interconnections, numerical computation representation, layer types, and other parameters to enable efficient encoding and decoding, allowing for application-specific optimizations and parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network parameters are encoded using standard container formats with full precision floating-point data types, then inference quality is maintained, but the coded size of NN representations becomes excessively large
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 precision and data type parameters, allowing significant reduction in coded size while maintaining acceptable inference quality through carefully designed quantization schemes that preserve critical parameter relationships.
Solution Approach 2:
The patent extracts and separates different components of neural network representations (parameters, graph structure, metadata) into distinct encoded forms. By extracting only the essential information needed for inference and using differential encoding for updates, the coded size is reduced while preserving inference quality.
2Measurement precision
If neural network parameters are transmitted and updated frequently to improve model performance, then inference quality can be enhanced, but bandwidth constraints become a serious bottleneck
Solution Approach 1:
The patent implements partial action by transmitting only the necessary portions of neural network parameters for updates rather than complete re-transmission. Differential encoding and update mechanisms allow selective transmission of changed parameters, reducing bandwidth consumption while maintaining the ability to enhance inference quality when updates are applied.
Solution Approach 2:
By changing the encoding parameters to use more compact representations and quantized formats, the patent enables more frequent parameter updates within available bandwidth constraints, allowing inference quality to be improved through regular model updates without being bottlenecked by data transmission capacity.
3Quantity of substance
If neural network representations are compressed to reduce coded size, then transmission efficiency improves, but computational inference complexity increases
Solution Approach 1:
The patent applies preliminary action by performing quantization and compression of neural network parameters during the training or preprocessing phase, before deployment. This preliminary transformation reduces the complexity of inference operations, as the quantized parameters require simpler computational operations compared to full-precision floating-point arithmetic.
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.