HalfV Framework Accelerates Multimodal LLM Inference by Addressing Visual Redundancy
A novel framework named HalfV has been introduced to enhance inference speed in high-resolution Multimodal Large Language Models (MLLMs), which often incur high computational expenses due to the explosion of visual tokens. The study, available on arXiv with the identifier 2604.16462v1, presents an innovative method to separate visual redundancy into two parts: universal Intrinsic Visual Redundancy (IVR) and architecture-specific Secondary Saturation Redundancy (SSR). This insight was gained through the analysis of truncated matrix entropy, uncovering a universal three-stage inference lifecycle across various model architectures. Unlike existing methods such as token pruning, which suffer from significant "backbone dependency," HalfV effectively reduces IVR via a unified pruning technique and adapts to SSR based on its unique characteristics in each architecture. Experimental findings indicate that HalfV offers improved efficiency-performance trade-offs compared to earlier approaches, addressing a crucial challenge in MLLM deployment with architecture-aware acceleration that preserves performance across different model backbones.
Key facts
- High-resolution Multimodal Large Language Models face prohibitive computational costs during inference
- Visual token explosion creates efficiency challenges for MLLMs
- Existing acceleration strategies suffer from "backbone dependency" issues
- Truncated matrix entropy analysis revealed a universal three-stage inference lifecycle
- Visual redundancy can be decoupled into Intrinsic Visual Redundancy and Secondary Saturation Redundancy
- HalfV framework uses unified pruning for IVR and adaptive handling for SSR
- Experiments show HalfV achieves superior efficiency-performance trade-offs
- The research addresses performance degradation when transferring acceleration methods between architectures
Entities
Institutions
- arXiv