Elastic Neural Networks for Constraint-Aware LLM Subnetwork Selection
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Solution Overview
Problem
Large language models face challenges in scenarios with limited memory and computational resources due to their substantial size, necessitating a trade-off between efficiency and accuracy, and existing customizable models often require fine-tuning for specific constraints.
Innovation Solution
An elastic neural network architecture with attention routers and perceptron routers dynamically selects sub-networks from an exponential number of candidates based on latency, memory, and accuracy targets, adapting to hardware and input variations without additional fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models with billions of parameters are used to achieve high accuracy in natural language processing, then model performance is improved, but memory and computational resource requirements increase significantly
Solution Approach 1:
The patent divides a single large language model into multiple smaller sub-networks (experts), each specializing in different aspects of language processing. Instead of deploying one massive model, the system segments the model into manageable pieces that can be selectively activated based on input requirements, reducing overall memory and computational footprint while maintaining accuracy.
Solution Approach 2:
The patent implements dynamic model architecture where the number and configuration of active sub-networks can be adjusted at runtime based on input complexity and resource availability. This dynamic adaptation allows the system to use more resources for complex tasks and fewer resources for simple tasks, resolving the contradiction between consistent high accuracy and variable resource constraints.
2Adaptability or versatility
If multiple dense models with different sizes are provided to satisfy efficiency-accuracy trade-offs, then adaptability to different resource constraints is improved, but model complexity and deployment difficulty increase
Solution Approach 1:
The patent creates a universal elastic neural network architecture that can function as multiple different model sizes through a single unified structure. The same base model can dynamically configure itself to operate as a small, medium, or large model depending on resource constraints, eliminating the need to maintain and deploy multiple separate dense models while achieving the same adaptability.
Solution Approach 2:
The patent implements a nested structure where smaller sub-networks are embedded within larger ones, forming a hierarchy of experts. This nested architecture allows the system to activate only the necessary level of complexity for each task, providing a streamlined path from simple to complex processing without requiring separate model deployments for each complexity level.
3Productivity
If customizable models with multiple sub-networks are developed to extract sub-models for specific constraints, then efficiency for specific scenarios is improved, but training complexity and fine-tuning requirements increase
Solution Approach 1:
The patent implements self-service mechanisms where the elastic neural network automatically configures its own sub-network activation patterns based on input characteristics and resource constraints, without requiring external fine-tuning for each deployment scenario. The model performs self-optimization during inference, selecting the appropriate subset of experts for each task, thereby eliminating complex fine-tuning processes while maintaining high efficiency for specific constraints.
Solution Approach 2:
The patent utilizes parameter changes in the router mechanisms that control sub-network activation. By adjusting router parameters and activation thresholds, the system can efficiently extract different sub-models from the same trained elastic network for different deployment constraints, avoiding the need for separate fine-tuning processes for each configuration.
Data Source
AI summary
Apparatuses, systems, and techniques to select, from an elastic neural network, a sub-network that satisfies deployment constraints. In at least one embodiment, a sub-network is selected from an elastic neural network by using routers trained to select candidate sets of attention heads for multi-head attention (MHA) sub-blocks and candidate sets of neurons for multi-layer perceptron (MLP) sub-blocks.