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AI Frontier Research

Innovative research pushing the boundaries of artificial intelligence

Pushing the boundaries of artificial intelligence through breakthrough research and collaborative innovation with leading global academic institutions. Exploring cutting-edge technologies in computer architecture, machine learning systems, and quantum computing.

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Research Focus Areas

Our interdisciplinary research covers cutting-edge areas of AI and computing technology

Computer Architecture

Developing next-generation AI-optimized processors and memory systems for unprecedented computational efficiency.

  • Neural Processing Units
  • Memory Optimization
  • Interconnect Design
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Machine Learning Systems

Advancing distributed training algorithms and optimization techniques for large-scale AI models.

  • Distributed Training
  • Model Compression
  • Federated Learning
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Quantum Computing

Exploring quantum-classical hybrid systems to solve complex optimization problems in AI.

  • Quantum Algorithms
  • Hybrid Systems
  • Error Correction
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Latest Publications

Peer-reviewed research papers and technical contributions

NIPS 20242024年12月

大规模分布式语言模型训练的新型架构优化方法

提出了一种创新的分布式训练架构，通过优化通信模式和内存管理，将大规模语言模型的训练效率提升了40%。该方法在GPT-4规模的模型上验证了其有效性，为大规模AI模型训练提供了新的解决方案。

作者：Dr. Sarah Chen, Dr. Michael Zhang, Dr. Alex Rodriguez
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ICML 20242024年11月

基于神经形态计算的低功耗深度学习加速器设计

设计了一种模拟生物神经元工作机制的新型计算芯片，能够在保持高精度的同时将深度学习推理的功耗降低85%,为边缘计算设备的AI应用提供了新的解决方案。

作者：Dr. Lisa Wang, Dr. James Kumar, Dr. Elena Petrov
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