Lightweight CNN Classification for 200,000-Character Telecom Logs
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Solution Overview
Problem
Existing network testing technologies struggle with the inefficiency, error-proneness, and scalability of manual analysis of complex telecommunications software logs, and state-of-the-art LLMs face challenges due to constrained context windows and high inference costs, failing to accurately classify defects in massive logs.
Innovation Solution
A compact CNN architecture with a sequence-to-sequence model and LSTM layers is used to initialize embeddings, followed by one-dimensional convolutional layers, capable of processing up to 200,000 characters, and a residual CNN for accurate defect triage, employing domain-specific pre-training and fine-tuning to enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of software logs is used, then human expertise can identify complex issues, but the process is inefficient and error-prone
Solution Approach 1:
The system enables automated self-service classification of software logs through CNN architecture, eliminating the need for manual human analysis while achieving high accuracy in defect triage across telecommunications protocol stacks
Solution Approach 2:
The patent replaces the mechanical human analysis process with an automated CNN-based machine learning system that processes software logs, substituting human cognitive operations with computational algorithms for defect classification
2Adaptability or versatility
If LLMs are used for log classification, then language understanding capability is improved, but context window constraints and high inference costs limit their effectiveness
Solution Approach 1:
The patent extracts and utilizes only the essential embedding capabilities from sequence-to-sequence models and LLMs, isolating the character embedding layer to initialize the CNN embedding layer, thereby obtaining language understanding power without the burden of full LLM architecture and context window limitations
Solution Approach 2:
The system creates a simplified copy of the embedding functionality from complex LLMs by initializing CNN embeddings with pre-trained character embeddings from sequence-to-sequence models, replicating the language understanding capability in a lightweight manner
3Measurement precision
If larger models are used for better accuracy, then classification performance improves, but deployment cost and computational resources increase
Solution Approach 1:
The patent changes the architectural parameters by using a compact CNN design with one-dimensional convolutional layers instead of large-scale transformer architectures, achieving comparable accuracy with significantly reduced computational parameters and energy consumption
Solution Approach 2:
The system employs a lightweight, disposable CNN model that can be deployed on edge devices with limited resources, replacing the need for expensive, resource-intensive large models while maintaining effective defect classification capabilities
Data Source
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AI summary
In some implementations, a device may provide a software log to a convolutional neural network (CNN) associated with software log classification, wherein the CNN is associated with an embedding layer that is initialized with character embeddings extracted from a sequence-to-sequence model, and the CNN is associated with a block of one-dimensional convolutional layers that follows the character embeddings. The device may generate a software log classification using the CNN, wherein the software log classification indicates whether the software log is associated with an issue and a telecommunications protocol stack in which the issue or a defect has occurred.