Autonomous driving paper index
THE EVOLUTION OF LARGE MODEL INFERENCE ARCHITECTURES: FROM CENTRALIZED CLOUDS TO DECENTRALIZED ON-DEVICE INTELLIGENCE
One-line summary
The proliferation of large-scale AI models, particularly Large Language Models (LLMs), has made inference a critical and resource-intensive workload.
Engineering notes
Key topics: autonomous driving, large language model. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The proliferation of large-scale AI models, particularly Large Language Models (LLMs), has made inference a critical and resource-intensive workload. This survey provides a comprehensive review of the historical evolution of inference architectures, charting a distinct trajectory from centralized, cloud-native paradigms to fully decentralized, on-device intelligence. We systematically analyze four key architectural epochs: (1) Device-Cloud, (2) Device-Edge-Cloud, (3) Device-Edge, and (4) pure On-Device inference. For each paradigm, we conduct an in-depth examination of its dominant systems, key enabling technologies, and the inherent advantages and limitations that catalyzed the transition to the subsequent stage. Our analysis reveals that this evolution is driven by a persistent set of trade-offs between computational power, latency, data privacy, cost, and energy efficiency. This paper concludes that the future of AI inference lies not in a single monolithic architecture but in a heterogeneous "compute continuum," where workloads are dynamically orchestrated across a spectrum of resources to meet diverse application demands.
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