AI Flow: Perspectives, Scenarios, and Approaches
🤖 AI Summary
To address the high resource consumption and communication bottlenecks of large language models (LLMs) in ubiquitous AI deployment, this paper proposes AI Flow—a three-tier collaborative architecture spanning devices, edge, and cloud. It introduces the novel “family-aligned models,” featuring multiple model sizes with unified feature spaces, and designs a connectivity-driven cross-node intelligence emergence mechanism. Furthermore, it integrates network-aware scheduling (unifying IT and CT perspectives) with communication-enhanced collaboration protocols. Compared to conventional approaches, AI Flow significantly reduces end-to-end inference latency and data transmission overhead, while enabling dynamic resource adaptation and cross-scenario transferability. Experimental validation across industrial control systems, vehicular platforms, and mobile terminals demonstrates its capability to deliver low-latency, highly elastic, and broadly scalable ubiquitous AI services. The framework establishes a new paradigm for large-scale edge AI deployment.
Application Category
📝 Abstract
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and communication technologies (IT/CT) has created an unbroken wave of connectivity and computation. This synergy has sparked a technological revolution, now reaching its peak with large artificial intelligence (AI) models that are reshaping industries and redefining human-machine collaboration. However, the realization of ubiquitous intelligence faces considerable challenges due to substantial resource consumption in large models and high communication bandwidth demands. To address these challenges, AI Flow has been introduced as a multidisciplinary framework that integrates cutting-edge IT and CT advancements, with a particular emphasis on the following three key points. First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters to optimize scalability and efficiency for low-latency model inference. Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features, enabling effective collaboration and the flexibility to adapt to varying resource constraints and dynamic scenarios. Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow. By leveraging communication networks to enhance connectivity, the collaboration among AI models across heterogeneous nodes achieves emergent intelligence that surpasses the capability of any single model. The innovations of AI Flow provide enhanced intelligence, timely responsiveness, and ubiquitous accessibility to AI services, paving the way for the tighter fusion of AI techniques and communication systems.
Problem
Research questions and friction points this paper is trying to address.
Optimize scalability and efficiency in AI model inference
Enable flexible AI collaboration under resource constraints
Achieve emergent intelligence through enhanced connectivity
Innovation
Methods, ideas, or system contributions that make the work stand out.
Device-edge-cloud framework for scalable low-latency AI
Familial models with aligned features for flexible deployment
Connectivity-driven emergent intelligence across heterogeneous nodes
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