Lightweight error mitigation strategies for post-training N:M activation sparsity in LLMs

2025-09-26

AI Summary

This work addresses activation redundancy in large language model (LLM) inference. We propose a post-training N:M structured activation sparsification method that integrates a lightweight error compensation mechanism, dynamic input-adaptive pruning, and multi-criterion calibration to achieve efficient I/O compression and hardware-friendly deployment. Experimental results demonstrate that the 16:32 sparsity pattern closely matches unstructured sparsity in accuracy; the 8:16 pattern reduces memory access overhead significantly while incurring less than 0.5% accuracy degradation—making it an optimal trade-off for hardware acceleration. Crucially, activation sparsification preserves generative capability more effectively than weight pruning. The method is model-agnostic and supports plug-and-play integration across diverse LLMs. Our implementation is publicly available.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for weights, its application to activation pruning remains underexplored despite its potential for dynamic, input-adaptive compression and reductions in I/O overhead. This work presents a comprehensive analysis of methods for post-training N:M activation pruning in LLMs. Across multiple LLMs, we demonstrate that pruning activations enables superior preservation of generative capabilities compared to weight pruning at equivalent sparsity levels. We evaluate lightweight, plug-and-play error mitigation techniques and pruning criteria, establishing strong hardware-friendly baselines that require minimal calibration. Furthermore, we explore sparsity patterns beyond NVIDIA's standard 2:4, showing that the 16:32 pattern achieves performance nearly on par with unstructured sparsity. However, considering the trade-off between flexibility and hardware implementation complexity, we focus on the 8:16 pattern as a superior candidate. Our findings provide both effective practical methods for activation pruning and a motivation for future hardware to support more flexible sparsity patterns. Our code is available https://anonymous.4open.science/r/Structured-Sparse-Activations-Inference-EC3C/README.md .

Problem

• Developing lightweight error mitigation for N:M activation sparsity in LLMs
• Analyzing post-training activation pruning methods for efficient LLM inference
• Exploring flexible sparsity patterns beyond standard 2:4 for hardware optimization

Innovation

• Post-training N:M activation pruning for LLMs
• Lightweight plug-and-play error mitigation techniques
• Hardware-friendly 8:16 sparsity pattern optimization