Neural Network Compression Priority Signaling for Critical Accuracy
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Solution Overview
Problem
Existing neural network compression methods often sacrifice accuracy across all aspects uniformly, failing to prioritize important aspects based on user preferences, leading to inefficient resource utilization.
Innovation Solution
Implementing a high-level syntax for priority signaling that allows users to specify and prioritize specific aspects of neural network compression, enabling targeted bit reduction and accuracy preservation or degradation based on user-defined preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If uniform compression is applied to all aspects of the neural network, then the overall size is reduced, but the accuracy of critical aspects is degraded
Solution Approach 1:
The patent segments the neural network into multiple aspects or tasks (e.g., different layers, filters, or functional components) and applies different compression parameters to each segment. This allows critical aspects to be preserved with higher accuracy while non-critical aspects are compressed more aggressively, resolving the contradiction between overall size reduction and preservation of critical accuracy.
Solution Approach 2:
The patent implements local quality by assigning different priority levels and compression parameters to different aspects of the neural network. Critical aspects are marked with higher priority and undergo less aggressive compression, while non-critical aspects are compressed more heavily. This localized differentiation resolves the contradiction by maintaining high quality where needed while achieving overall compression.
2Productivity
If aggressive compression is applied to reduce neural network size, then resource utilization is improved, but task performance varies across different aspects
Solution Approach 1:
The patent introduces dynamic priority signaling that allows the system to adapt compression parameters based on the importance of different neural network aspects. By dynamically adjusting compression levels according to priority markers, the system achieves better resource utilization while maintaining reliable task performance for critical aspects.
Solution Approach 2:
The patent changes compression parameters (such as quantization precision, pruning thresholds, or compression ratios) based on the priority of different neural network aspects. Critical aspects use parameters that preserve performance, while non-critical aspects use parameters that maximize compression, thus resolving the contradiction between resource utilization and task performance.
3Loss of time
If compression parameters are optimized for overall size reduction, then transmission efficiency is improved, but accuracy preservation becomes uniform and suboptimal
Solution Approach 1:
The patent segments the neural network transmission into priority-based groups, allowing different aspects to be transmitted with different compression levels. This segmentation enables optimized transmission time for the overall network while preserving accuracy for critical aspects, resolving the contradiction between transmission efficiency and accuracy preservation.
Solution Approach 2:
The patent introduces priority markers or metadata as an intermediary between the neural network structure and compression algorithm. This intermediary enables the system to differentiate between critical and non-critical aspects during transmission, achieving both efficient transmission time and selective accuracy preservation.
Data Source
AI summary
Apparatuses, methods, and computer programs for compressing a neural network are disclosed. An apparatus includes at least one processor; and at least one non-transitory memory including computer program code, the memory and the computer program code configured to, with the at least one processor, cause the apparatus to: receive information from a second device, where the information comprises at least one parameter configured to be used for compression of a neural network, where the at least one parameter is in regard to at least one first aspect or task of the neural network; and compress the neural network, where the neural network is compressed based, at least partially, upon the at least one parameter received from the second device. The apparatus may also receive a compressed neural network from the second device, and further compress the compressed neural network based on the information.