18 citations · 21 across the 21 of their papers we have counts for
9 papers · 1 filter
PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization
Tianshi Xu, Shuzhang Zhong, Wenxuan Zeng +2
Private deep neural network (DNN) inference based on secure two-party computation (2PC) enables secure privacy protection for both the server and the client. However, existing secu…
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
Shuzhang Zhong, Ling Liang, Yuan Wang +3
Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands. However, their d…
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference
Chenqi Lin, Tianshi Xu, Zebin Yang +3
With the fast evolution of large language models (LLMs), privacy concerns with user queries arise as they may contain sensitive information. Private inference based on homomorphic…
PrivCirNet: Efficient Private Inference via Block Circulant Transformation
Tianshi Xu, Lemeng Wu, Runsheng Wang +1
Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the…
EQO: Exploring Ultra-Efficient Private Inference with Winograd-Based Protocol and Quantization Co-Optimization
Wenxuan Zeng, Tianshi Xu, Meng Li +1
Private convolutional neural network (CNN) inference based on secure two-party computation (2PC) suffers from high communication and latency overhead, especially from convolution l…
ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
Shuzhang Zhong, Zebin Yang, Meng Li +3
Recent advancements in generative large language models (LLMs) have significantly boosted the performance in natural language processing tasks. However, their efficiency is hampere…
ASCEND: Accurate yet Efficient End-to-End Stochastic Computing Acceleration of Vision Transformer
Tong Xie, Yixuan Hu, Renjie Wei +4
Stochastic computing (SC) has emerged as a promising computing paradigm for neural acceleration. However, how to accelerate the state-of-the-art Vision Transformer (ViT) with SC re…
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference
Tianshi Xu, Meng Li, Runsheng Wang
Secure two-party computation with homomorphic encryption (HE) protects data privacy with a formal security guarantee but suffers from high communication overhead. While previous wo…
Efficient yet Accurate End-to-End SC Accelerator Design
Meng Li, Yixuan Hu, Tengyu Zhang +4
Providing end-to-end stochastic computing (SC) neural network acceleration for state-of-the-art (SOTA) models has become an increasingly challenging task, requiring the pursuit of…