Neural Network Serialization Formats for Smaller Model Updates
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Solution Overview
Problem
The sheer amount of data required to represent neural networks, particularly the weights and biases, hampers efficient transmission and updating, leading to bottlenecks in data rate and computational complexity.
Innovation Solution
A data stream representation of neural networks is encoded with serialization parameters, numerical computation representation parameters, layer type parameters, pointers, and other indicators to facilitate efficient encoding and decoding, allowing for application-specific coding orders and parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network parameters are represented using large floating-point data types to maintain precision, then inference quality is preserved, but the coded size of the representation increases significantly
Solution Approach 1:
The patent applies parameter changes by dynamically selecting different data types (float32, float16, int8, uint8) for representing neural network parameters based on layer type and operational requirements. This allows the system to reduce coded size by using lower-precision types where appropriate while maintaining inference quality where high precision is needed.
Solution Approach 2:
The patent segments the neural network representation into different data type categories (weights, biases, activations) and applies different precision levels to each segment. This selective precision approach reduces the overall coded size while preserving inference quality in critical segments.
2Reliability
If full precision neural network parameters are transmitted and updated frequently, then model accuracy is maintained, but data rate becomes a bottleneck
Solution Approach 1:
The patent enables dynamic adjustment of parameter precision based on transmission requirements and computational needs. By changing the data type representation from float32 to lower-precision formats, the system reduces data rate requirements and enables more frequent model updates and transmissions without sacrificing model accuracy.
3Measurement precision
If high precision floating-point parameters are used throughout the network, then computational accuracy is maintained, but computational complexity increases
Solution Approach 1:
The patent applies local quality by assigning different precision levels to different layers and operations within the neural network. Critical layers requiring high accuracy use float32 or float16, while less critical layers use int8 or uint8 representations. This local differentiation maintains computational accuracy where needed while reducing overall computational complexity.
4Reliability
If lossless compression is applied to neural network parameters, then exact representation is preserved, but compression ratio is limited
Solution Approach 1:
The patent transforms the representation parameters from high-precision floating-point formats to lower-precision integer formats where applicable. This parameter transformation enables significant compression ratios while maintaining sufficient representation fidelity for accurate inference, effectively converting a lossless compression problem into a controlled lossy compression scenario.
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.