Neural Network Data Compression via Pseudo Video Conversion
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Solution Overview
Problem
As neural networks scale, the increasing amount of weight and bias values demands higher storage performance and memory access bandwidth, which existing technologies have not adequately addressed.
Innovation Solution
A neural network system that converts neural network content values into pseudo video data, which is then encoded into neural network data packages using a compression module, leveraging spatial correlation to reduce data size and improve transmission efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural network scale increases, then computational capability and accuracy improve, but storage requirements and memory access bandwidth demands increase
Solution Approach 1:
The patent applies parameter changes by transforming neural network weight values from their original format into pseudo-video data format with different data characteristics. This conversion allows the same neural network data to be compressed using video compression techniques, effectively reducing storage requirements while maintaining the computational capability of the neural network.
Solution Approach 2:
The patent creates a copy of the neural network data in the form of pseudo-video data. This copy is then compressed using video compression algorithms, allowing the original neural network data to be stored more efficiently without losing any information needed for computational operations.
2Productivity
If neural network data size increases, then model accuracy improves, but memory access bandwidth requirements increase
Solution Approach 1:
The patent changes the data representation parameters by converting neural network weights into pseudo-video data format. This transformation enables the use of efficient video compression algorithms that reduce data size while maintaining information integrity, thereby reducing memory access bandwidth requirements without compromising model accuracy.
3Ease of operation
If neural network data is transmitted, then model deployment improves, but transmission bandwidth requirements increase
Solution Approach 1:
The patent creates a compressed copy of the neural network data in pseudo-video format that can be transmitted efficiently. This compressed representation maintains all necessary information for model deployment while significantly reducing the transmission bandwidth required, making model deployment more practical for resource-constrained environments.
Data Source
AI summary
Aspects of data compression/decompression for neural networks are described herein. The aspects may include a model data converter configured to convert neural network content values into pseudo video data. The neural network content values may refer to weight values and bias values of the neural network. The pseudo video data may include one or more pseudo frames. The aspects may further include a compression module configured to encode the pseudo video data into one or more neural network data packages.