Paper 2025/765

ZKPoG: Accelerating WitGen-Incorporated End-to-End Zero-Knowledge Proof on GPU

Abstract

Zero-Knowledge Proof (ZKP) is a cornerstone technology in privacy-preserving computing, addressing critical challenges in domains such as finance and healthcare by ensuring data confidentiality during computation. However, the high computational overhead of ZKP, particularly in proof generation and verification, limits its scalability and usability in real-world applications. Existing efforts to accelerate ZKP primarily focus on specific components, such as polynomial commitment schemes or elliptic curve operations, but fail to deliver an integrated, flexible, and efficient end-to-end solution that includes witness generation. In this work, we present ZKPoG, a GPU-based ZKP acceleration platform that achieves full end-to-end optimization. ZKPoG addresses three key challenges: (1) designing a witness-generation-incorporated flow for Plonkish circuits, enabling seamless integration of frontend and backend with GPU acceleration; (2) optimizing memory usage to accommodate large-scale circuits on affordable GPUs with limited memory; and (3) introducing an automated compiler for custom gates, simplifying adaptation to diverse applications. Experimental results on an NVIDIA RTX 4090 GPU show on average $$ end-to-end acceleration compared to state-of-the-art CPU implementations and on average $$ speedup over existing GPU-based approaches.