Papers
1
Total Citations
17
H-Index
1
About
Xing Hu is a computer architecture researcher specializing in efficient hardware design for artificial intelligence and machine learning workloads, with a particular focus on enabling large-scale AI inference at the edge. His most notable work, "Cambricon-LLM," addresses one of the most pressing challenges in modern AI deployment: running billion-parameter large language models on resource-constrained edge devices such as smartphones and robotics platforms. By proposing a chiplet-based hybrid architecture, Hu and his collaborators tackled the fundamental tension between the enormous computational and memory demands of 70-billion-parameter LLMs and the strict power and area budgets of on-device hardware. This work directly advances goals of user privacy, network resilience, and democratized AI access — making it highly relevant to both industry and academia. Garnering 17 citations shortly after its 2024 publication, the paper signals rapid community uptake in the fast-moving field of edge AI accelerators. Hu's research sits at the intersection of chip architecture, system-level co-design, and practical AI deployment, positioning him as an emerging contributor shaping how next-generation neural network inference hardware will be designed for real-world, latency-sensitive applications.
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Top Papers
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