Neural Network Bitstream Syntax for Incremental Weight Update Compression
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Solution Overview
Problem
Current multimedia transport and neural network technologies lack efficient mechanisms for incremental weight update compression, which is essential for effective communication and processing of neural networks, particularly in scenarios requiring incremental updates and topology changes.
Innovation Solution
The proposed solution involves an apparatus and method that encode or decode high-level bitstream syntax for neural networks, incorporating mechanisms for incremental weight update compression, including signaling modes, quantization algorithms, and topology changes, using flags and data units to facilitate efficient compression and decompression of neural network representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standardized formats for neural network exchange are used, then compatibility and ease of operation are improved, but the ability to efficiently handle incremental weight updates and topology changes deteriorates
Solution Approach 1:
The patent introduces dynamic syntax elements and flags within the standardized NNR bitstream format that enable adaptive switching between different weight update compression modes. The high-level bitstream syntax includes optional elements that can be dynamically activated based on whether incremental updates or topology changes are present, allowing the system to maintain standardization while adapting to specific update scenarios.
Solution Approach 2:
The patent segments the weight update data into distinct components within the bitstream structure, separating incremental weight updates from topology change information. This segmentation allows each component to be processed and compressed independently using appropriate mechanisms, while still being contained within a unified standardized format that maintains compatibility.
2Adaptability or versatility
If incremental weight update compression mechanisms are added to handle topology changes, then adaptability and productivity are improved, but device complexity increases
Solution Approach 1:
The patent designs the high-level bitstream syntax to serve multiple functions: it can represent complete neural network models, incremental weight updates, and topology changes all within a single standardized format. The syntax elements are designed to be universally applicable across different update scenarios, reducing the need for separate specialized formats and thereby limiting complexity growth.
Solution Approach 2:
The patent uses copying mechanisms where the high-level bitstream syntax structure mirrors existing standardized neural network representation formats. By reusing and adapting proven syntax patterns from existing standards rather than creating entirely new complex structures, the patent minimizes the increase in device complexity while still providing the needed incremental update capabilities.
3Manufacturing precision
If quantization algorithms and dithering mechanisms are implemented, then manufacturing precision and measurement precision are improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent introduces syntax elements that allow dynamic configuration of quantization parameters such as bit depth, precision levels, and dithering strength. These parameters can be adjusted based on the specific requirements of the weight update data being compressed, allowing the system to achieve high precision when needed while reducing complexity for less demanding scenarios. The high-level syntax provides a framework for specifying these parameters without hardcoding complex algorithms.
Data Source
AI summary
An example apparatus, method, and 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 neural network representation (NNR) bitstream comprises one or more of the at least one information units, and wherein the syntax definitions provide one or more mechanisms for introducing a weight update compression interpretation into the NNR bitstream.