Highlights:
- New architecture ‘Focus’ boosts Vision-Language Model (VLM) efficiency by 2.4x performance and 3.3x energy savings.
- Developed by researchers Chiyue Wei, Cong Guo, Junyao Zhang, Haoxuan Shan, Yifan Xu, Ziyue Zhang, Yudong Liu, Qinsi Wang, Changchun Zhou, Hai ‘Helen’ Li, and Yiran Chen.
- Focus employs hierarchical redundancy elimination across semantic, spatial-temporal, and vector levels.
- Designed for real-time, streaming-friendly execution on modern AI accelerators.
TLDR:
Researchers have unveiled ‘Focus’, a groundbreaking streaming concentration architecture that significantly accelerates Vision-Language Models. By eliminating redundant computations at multiple levels, Focus achieves 2.4x speed and 3.3x energy efficiency improvements, paving the way for faster and more efficient deployment of AI models on edge and data-center hardware.
The research team led by Chiyue Wei, Cong Guo, Junyao Zhang, Haoxuan Shan, Yifan Xu, Ziyue Zhang, Yudong Liu, Qinsi Wang, Changchun Zhou, Hai ‘Helen’ Li, and Yiran Chen has introduced **Focus: A Streaming Concentration Architecture for Efficient Vision-Language Models**, a breakthrough approach addressing the heavy computational and memory demands of Vision-Language Models (VLMs). VLMs, which bridge computer vision and natural language understanding, power applications like video captioning and visual question answering. However, as these models scale, they face serious performance bottlenecks and excessive energy consumption, making real-time inference difficult on current hardware accelerators.
Focus redefines how VLMs process multimodal data by introducing a **multilevel concentration framework** that systematically eliminates redundant information at finer granularity. The architecture operates on three layers: (1) **semantic-guided token pruning** aligns inputs with the context provided by textual prompts, removing irrelevant tokens before deeper processing; (2) **spatial-temporal block-level concentration** uses localized comparisons across video frames to detect redundant visual data; and (3) **vector-level redundancy removal** employs motion-aware matching to reduce unnecessary vector computations. This hierarchical compression strategy ensures that only the most meaningful data is propagated through the inference pipeline, minimizing computational waste and enhancing throughput.
From a hardware perspective, Focus integrates computational efficiency with architectural innovation. Using GEMM (General Matrix Multiply) tiling, convolution-style data layouts, and cross-modal attention mechanisms, it enables **streaming-friendly, on-chip execution**. The system’s design aligns with systolic-array accelerators commonly used in AI chips, making it ideal for scalable hardware deployment. The research reports a **2.4x speedup** and a **3.3x reduction in energy consumption** compared to state-of-the-art accelerators, underlining Focus’s potential to transform how complex AI workloads are deployed in both edge devices and data centers. With its full-stack open-source release on GitHub, the Focus architecture not only demonstrates academic innovation but also provides an accessible platform for further real-world advancements in AI hardware acceleration.
Source:
https://arxiv.org/abs/2512.14661