Three Papers Accepted by DAC'2024
Three papers on efficient and privacy-preserving deep learning are accepted by DAC'2024 as regular papers, includin “Alchemist: A Unified Accelerator Architecture for Cross-Scheme Fully Homomorphic Encryption”, “FastQuery: Communication-efficient Embedding Table Query for Private LLMs inference”, and “MoteNN: Memory Optimization via Fine-grained Scheduling for Deep Neural Networks on Tiny Devices”.
李萌
助理教授、研究员、博雅青年学者
李萌，北京大学人工智能研究院和集成电路双聘助理教授、研究员、博雅青年学者。他的研究兴趣集中于高效、安全的多模态人工智能加速算法和芯片，旨在通过算法到芯片的跨层次协同设计和优化，为人工智能构建高能效、高可靠、高安全的算力基础。