5 papers
GPU Acceleration of TFHE-Based High-Precision Nonlinear Layers for Encrypted LLM Inference
Guoci Chen, Xiurui Pan, Qiao Li +5
Deploying large language models (LLMs) as cloud services raises privacy concerns as inference may leak sensitive data. Fully Homomorphic Encryption (FHE) allows computation on encr…
CipherPrune: Efficient and Scalable Private Transformer Inference
Yancheng Zhang, Jiaqi Xue, Mengxin Zheng +4
Private Transformer inference using cryptographic protocols offers promising solutions for privacy-preserving machine learning; however, it still faces significant runtime overhead…
ALLMod: Exploring rea-Efficiency of UT-based arge Number ular Reduction via Hybrid Workloads
Fangxin Liu, Haomin Li, Zongwu Wang +5
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width…
Trinity: A General Purpose FHE Accelerator
Xianglong Deng, Shengyu Fan, Zhicheng Hu +9
In this paper, we present the first multi-modal FHE accelerator based on a unified architecture, which efficiently supports CKKS, TFHE, and their conversion scheme within a single…
Taiyi: A high-performance CKKS accelerator for Practical Fully Homomorphic Encryption
Shengyu Fan, Xianglong Deng, Zhuoyu Tian +5
Fully Homomorphic Encryption (FHE), a novel cryptographic theory enabling computation directly on ciphertext data, offers significant security benefits but is hampered by substanti…