arxiv_cl 90% Match Research Paper ML Engineers,Researchers in Model Optimization,Developers of Edge AI Applications,LLM Developers 17 hours ago

IG-Pruning: Input-Guided Block Pruning for Large Language Models

large-language-models › model-architecture

📄 Abstract

Abstract: With the growing computational demands of large language models (LLMs), efficient inference has become increasingly critical for practical deployment. Depth pruning has emerged as a promising approach for reducing the computational costs of large language models by removing transformer layers. However, existing methods typically rely on fixed block masks, which can lead to suboptimal performance across different tasks and inputs. In this paper, we propose IG-Pruning, a novel input-aware block-wise pruning method that dynamically selects layer masks at inference time. Our approach consists of two stages: (1) Discovering diverse mask candidates through semantic clustering and L0 optimization, and (2) Implementing efficient dynamic pruning without the need for extensive training. Experimental results demonstrate that our method consistently outperforms state-of-the-art static depth pruning methods, making it particularly suitable for resource-constrained deployment scenarios.

Key Contributions

This paper proposes IG-Pruning, a novel input-aware block-wise pruning method for LLMs that dynamically selects layer masks at inference time. Unlike static pruning, IG-Pruning discovers diverse mask candidates through semantic clustering and L0 optimization, enabling efficient dynamic pruning without extensive retraining. This approach consistently outperforms static methods, making it highly suitable for resource-constrained deployment scenarios.

Business Value

Enables the deployment of powerful LLMs on devices with limited computational resources (e.g., smartphones, edge devices), opening up new application possibilities and reducing cloud dependency.

Paper Metadata

Innovation Type

Algorithmic Method

Deployment Feasibility

High, specifically targets deployment scenarios.

Limitations Addressed

Suboptimal performance of fixed block masks in depth pruning,Inflexibility of static pruning across different tasks and inputs,High computational cost of LLM inference

Performance Gains

Consistently outperforms state-of-the-art static depth pruning methods.

Technical Tags

LLM pruningdepth pruninginput-awaredynamic pruningtransformer layersinference efficiencyresource-constrainedsemantic clusteringL0 optimizationstatic pruning

Research Topics

Model CompressionEfficient Deep LearningLLM InferenceNeural Network Architecture SearchHardware Acceleration

Methods & Architectures

Input-Guided Pruning (IG-Pruning)Dynamic Layer Mask SelectionSemantic ClusteringL0 OptimizationMask Candidate Discovery Transformer ModelsLarge Language Models (LLMs)

Applications & Tasks

Edge AI Mobile Computing Efficient LLM Deployment Real-time AI High computational demands of LLMsSuboptimal performance of static pruning methodsNeed for efficient inference in resource-constrained environments Reducing LLM Inference CostEnabling LLMs on Edge DevicesDynamic Model Adaptation

Related Fields

Machine LearningDeep LearningNatural Language ProcessingComputer ArchitectureModel Compression

Keywords

LLM pruningdepth pruninginput-awaredynamic pruninginferenceefficiencyresource-constrainedtransformermodel compressionoptimizationedge AIsemantic clustering

Academic Context

#Model Compression#Efficient Deep Learning#LLM Inference#Neural Network Architecture Search#Hardware Acceleration

Commercial Potential

Potential Products

Optimized LLM Libraries for Edge DevicesOn-Device AI AssistantsEfficient AI Inference Engines

Target Industries

TechnologyMobileAutomotiveIoTConsumer Electronics

Use Case Examples

Running advanced language models on smartphones for real-time translation or summarizationEnabling AI features in smart home devices with limited processing powerDeploying LLMs in autonomous vehicles for natural language interaction

Competitive Edge

Offers a dynamic, input-aware approach to depth pruning that surpasses static methods in performance and adaptability.

Market Opportunity

Large and growing market for efficient AI deployment.

Revenue Models

Licensing of optimized modelsconsulting services.

Resource Requirements

Compute Needs

Reduced compute requirements at inference time.

Data Requirements

Requires diverse inputs to effectively determine dynamic masks.

Deployment Constraints

The dynamic selection adds a small overhead at inference, but is designed to be efficient.

Scalability

The method is designed to scale pruning effectiveness across different LLM sizes and tasks.

Production Readiness

Maturity Level

Algorithmic Improvement

Time to Market

1-2 years for integration into inference optimization libraries

Licensing

Likely open-source algorithm.

Patent Potential

Moderate (for the IG-Pruning methodology)

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