Seven Papers Accepted by ICCAD'2024

Seven papers, including 2 papers on privacy-preserving AI and 5 papers on efficient AI, are accepted by ICCAD'2024 as regular papers.

Papers on privacy-preserving AI:

  • PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization
  • FlexHE: A flexible Kernel Generation Framework for Homomorphic Encryption-Based Private Inference

Papers on efficient AI:

  • OSCA: End-to-end Serial Stochastic Computing Neural Acceleration with Fine-grained Scaling and Piecewise Activation
  • HG-PIPE: Vision Transformer Acceleration with Hybrid-Grained Pipeline
  • ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
  • AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
  • MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers
Meng Li
Meng Li
Assistant Professor

I am currently a tenure-track assistant professor jointly affiliated with the Institute for Artificial Intelligence and School of Integrated Circuits in Peking University. My research interests focus on efficient and secure multi-modality AI acceleration algorithms and hardwares.

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