Highlights:
- Introduces Focus, a Streaming Concentration Architecture for Vision-Language Models (VLMs).
- Delivers 2.4x faster inference and 3.3x energy reduction compared to existing accelerators.
- Utilizes a hierarchical compression strategy across semantic, spatial-temporal, and vector levels.
- Enables real-time, on-chip execution for high-efficiency visual-language processing.
TLDR:
A new architecture called Focus streamlines Vision-Language Model inference by progressively removing redundancy at multiple levels, delivering faster and more energy-efficient AI computations. It sets a breakthrough benchmark for real-time, hardware-optimized multimodal learning.
Researchers 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 have unveiled **Focus**, a groundbreaking Streaming Concentration Architecture designed to make Vision-Language Models (VLMs) dramatically more efficient. The study, published on arXiv, tackles one of the key bottlenecks in deploying large-scale multimodal AI systems — their enormous computational and memory demands.
VLMs, which combine visual and linguistic understanding for applications such as video captioning and visual question answering, often require processing vast sequences of video data alongside textual prompts. This creates excessive redundancy that strains hardware resources. Existing methods like token pruning or merging tend to operate at a coarse level, incurring overhead from global token operations. Focus confronts this limitation through a fine-grained, hierarchical compression approach. It introduces a three-tier concentration paradigm: semantic-guided token pruning influenced by textual input, localized spatial-temporal concentration via block-level comparisons, and motion-aware matching to eliminate redundant vector computations.
From a hardware perspective, Focus is meticulously co-designed for **streaming-friendly, on-chip execution**, aligning architecture-level and algorithmic optimization. The system leverages GEMM tiling, convolution-style layouts, and cross-modal attention mechanisms to curtail off-chip data transfers, thereby boosting throughput while conserving energy. Implemented as a modular addition to a systolic-array accelerator, Focus achieves a 2.4× speedup and a 3.3× energy reduction over current state-of-the-art designs. This positions it as a major advancement in efficient edge-side AI inference, paving the way for more scalable, low-latency VLM deployment. The team has open-sourced the full implementation at [https://github.com/dubcyfor3/Focus], encouraging collaboration and exploration in the AI hardware community.
The introduction of Focus marks a vital milestone in bridging the efficiency gap between software-level AI innovation and hardware execution. By focusing computational effort where it matters most—reducing redundancies progressively and intelligently—it redefines how next-generation multimodal systems could seamlessly integrate high performance and energy efficiency.
Source:
https://doi.org/10.48550/arXiv.2512.14661