Neural Network Bitstream Syntax Encoding Interoperability
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Solution Overview
Problem
Current technologies lack a standardized and efficient method for encoding and decoding high-level bitstream syntax for neural networks, particularly in terms of interoperability and compression, which hinders the effective transmission and storage of neural network data across different systems.
Innovation Solution
The proposed solution involves an apparatus and method that encode or decode high-level bitstream syntax for neural networks, using information units with syntax definitions for neural networks or their portions, and include features like sparsification thresholds, performance mapping, and context reset flags to enable efficient compression and interoperability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a standardized format for neural network exchange is implemented, then interoperability between different systems is improved, but device complexity increases due to the need to maintain and process standardized syntax structures
Solution Approach 1:
The bitstream syntax is segmented into distinct information units, each with specific syntax definitions. This segmentation allows different systems to process only the relevant units they need, reducing the effective complexity each device must handle while maintaining full interoperability through the standardized structure.
Solution Approach 2:
The standardized bitstream syntax structure serves multiple functions: it enables interoperability between different neural network systems, provides a framework for compression, and allows for extensibility through optional syntax elements. This universal structure reduces the need for multiple specialized formats across different systems.
2Productivity
If compression techniques are applied to neural network data, then data transmission and storage efficiency is improved, but loss of information may occur that affects neural network accuracy
Solution Approach 1:
The syntax structure includes parameters that allow dynamic adjustment of compression levels and precision for different neural network data elements. By changing parameters such as precision levels for weights and biases, or enabling/disabling optional syntax elements, the system can optimize between compression ratio and accuracy retention based on specific requirements.
Solution Approach 2:
The standardized syntax structure prepares neural network data for compression by organizing it into information units with defined properties before compression is applied. This preliminary organization allows compression algorithms to work more efficiently while preserving critical information, reducing the risk of accuracy loss during the compression process.
3Manufacturing precision
If detailed syntax definitions are included for all neural network components, then manufacturing precision of the bitstream format is improved, but the size and complexity of the serialized bitstream increases
Solution Approach 1:
The syntax structure allows for partial inclusion of syntax elements based on what is actually needed for a given neural network representation. Not all syntax elements are required for every neural network - the structure enables selective inclusion of only the necessary precision and detail levels, maintaining manufacturing precision where needed while reducing overall bitstream size.
Data Source
AI summary
In example embodiments, an apparatus, a method, and a computer program product are provided. The apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform: encode or decode a high-level bitstream syntax for at least one neural network; wherein the high-level bitstream syntax comprises at least one information unit, wherein the at least one information unit comprises syntax definitions for the at least one neural network or a portion of the at least one neural network; and wherein a serialized bitstream comprises one or more of the at least one information units.