Hierarchical LLM Pruning for Low-Latency Telecom Inference
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Solution Overview
Problem
Telecommunication networks face challenges with resource constraints, latency, and network efficiency due to deploying resource-intensive generative AI models at cell-tower sites, leading to potential performance bottlenecks and service disruptions.
Innovation Solution
Implement hierarchical and peer pruning strategies using a large language model (LLM) with helper models trained at central locations, distributing pruned models across network layers, and utilizing caching and peer collaboration to optimize processing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If generative AI models are deployed at cell-tower sites, then real-time processing capability is improved, but resource constraints and network efficiency deteriorate
Solution Approach 1:
The system segments the AI model into two parts: a pruned model deployed at edge cell-tower sites for real-time processing, and a helper model kept at centralized locations for complex tasks. This segmentation allows real-time processing at the edge while avoiding the resource burden of deploying the complete model everywhere.
Solution Approach 2:
The helper model acts as an intermediary between the pruned edge model and the centralized system. When the pruned model encounters tasks beyond its capability, it queries the helper model, which provides guidance or direct answers, thus enabling real-time processing with fallback to centralized resources only when necessary.
2Measurement precision
If full LLM is deployed at edge computing nodes, then processing accuracy is improved, but device complexity and resource consumption increase
Solution Approach 1:
The system extracts and removes unnecessary parameters from the original LLM to create a pruned model suitable for edge deployment. This extraction process retains the essential processing capabilities needed for accurate inference while removing redundant components that contribute to complexity and resource consumption.
Solution Approach 2:
The pruning process changes the parameters of the model by selectively removing weights and connections based on their importance. This parameter transformation creates a simplified model structure that maintains accuracy for common tasks while reducing overall complexity and resource requirements.
3Use of energy by moving object
If pruning is applied to reduce model size, then resource efficiency is improved, but inference accuracy may deteriorate
Solution Approach 1:
The helper model serves as an intermediary that compensates for the accuracy limitations of the pruned model. When the pruned model is uncertain or encounters complex queries, it queries the helper model for assistance, thereby maintaining high inference accuracy while still benefiting from the resource efficiency of the pruned model for routine tasks.
Solution Approach 2:
Instead of fully pruning the model to maximum compression, the system applies partial pruning that retains sufficient accuracy for the majority of tasks. The helper model provides additional support when needed, effectively using a combination of partial pruning and selective full-model consultation to maintain accuracy while improving resource efficiency.
Data Source
AI summary
Provided are a method, system, and computer program product for hierarchical inference utilizing a large language model (LLM). Training is performed at a central location, of a helper model and a pruned model for each layer of a hierarchy, wherein the helper model is trained to classify a request as appropriate for the pruned model, and wherein the pruned model is generated from a reduction process of the LLM. A process distributes the helper model and pruned model to different levels of the hierarchy. The process directs, by utilizing the helper model at each level of the hierarchy, inference generation to the pruned model or to another model at a higher tier.