Neural Network Bitstream Headers for Independent GON Decoding
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Solution Overview
Problem
Current encoding and decoding techniques for neural network data lack efficient methods for representing and reconstructing neural networks with varying topologies, leading to inefficiencies in parameter set and header design, which hinders independent decoding and aggregation of neural network units.
Innovation Solution
The introduction of a 'group of NNR units' (GON) concept, where each GON is self-contained and independently decodable, comprising a neural network model parameter set, layer parameter set, topology unit, and quantization unit, with syntax elements to indicate unit types and references, allowing for efficient decoding and aggregation within aggregate NNR units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current encoding and decoding techniques are used for neural network data, then the existing parameter set and header design can be maintained, but efficient representation and reconstruction of neural networks with varying topologies is not achieved, hindering independent decoding and aggregation
Solution Approach 1:
The patent divides the neural network representation into distinct units (NNR units) that can be independently decoded. Each unit contains specific parameter sets and headers tailored to its function, allowing flexible composition for different network topologies without requiring complex global parameter structures.
Solution Approach 2:
The parameter set and header design is made dynamic and adaptive to different neural network topologies. The NNR units can be selectively assembled and configured based on the specific topology requirements, enabling the system to adapt to varying network structures efficiently.
2Productivity
If neural network units are designed for independent decoding, then decoding efficiency is improved, but the design complexity of parameter sets and headers increases
Solution Approach 1:
By segmenting the neural network data into independent NNR units with self-contained parameter sets and headers, each unit can be decoded independently without requiring the entire bitstream. This segmentation enables parallel processing and improves decoding efficiency while keeping individual unit designs manageable.
Solution Approach 2:
The patent uses standardized templates and repeated structures for NNR units, where common parameter sets and header formats are copied and adapted across different units. This reduces the overall design complexity by reusing proven structures rather than creating unique designs for each unit.
3Loss of substance
If aggregate NNR units are used for compression, then data compression efficiency is improved, but the ability to represent diverse neural network topologies is constrained
Solution Approach 1:
The patent implements a nested structure where NNR units are contained within aggregate NNR units, which can themselves be nested within larger structures. This hierarchical nesting allows for efficient compression at multiple levels while preserving the ability to represent diverse topologies through the combination and configuration of nested units.
Solution Approach 2:
The aggregate NNR unit design is made universal and multi-functional, capable of containing different types of NNR units and representing various neural network topologies through flexible configuration. The same aggregate structure can adapt to different topology requirements by changing its contents rather than requiring topology-specific structures.
Data Source
AI summary
Systems and methods for encoding and decoding neural network data is provided. A method includes: receiving a neural network representation (NNR) bitstream including a group of NNR units (GON) that represents an independent neural network with a topology, the GON including an NNR model parameter set unit, an NNR layer parameter set unit, an NNR topology unit, an NNR quantization unit, and an NNR compressed data unit; and reconstructing the independent neural network with the topology by decoding the GON.