BatchZK: A Fully Pipelined GPU-Accelerated System for Batch Generation of Zero-Knowledge Proofs
Tao Lu, Yuxun Chen, Zonghui Wang, Xiaohang Wang, Wenzhi Chen, Jiaheng Zhang
2025年份
11被引次数
6顶会引用
摘要
Zero-knowledge proof (ZKP) is a cryptographic primitive that enables one party to prove the validity of a statement to other parties without disclosing any secret information. With its widespread adoption in applications such as blockchain and verifiable machine learning, the demand for generating zero-knowledge proofs has increased dramatically. In recent years, considerable efforts have been directed toward developing GPU-accelerated systems for proof generation. However, these previous systems only explored efficiently generating a single proof by reducing latency rather than batch generation to provide high throughput.
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- Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge ProofsAlhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bünz 等ISCA 2025 · 被引用 12 次
- zkPHIRE: A Programmable Accelerator for ZKPs over HIgh-degRee, Expressive GatesAlhad Daftardar, Jianqiao Mo, Joey Ah-kiow, Benedikt Bünz 等HPCA 2026 · 被引用 1 次
- TensorZKP: Repurposing GPU Tensor Cores for High-Performance Zero-Knowledge ProofsTao Lu, Jipeng Zhang, Yanpei Guo, Xuanming Liu 等USENIX Security 2026
- Scalable Collaborative zk-SNARK and Its Application to Fully Distributed Proof DelegationXuanming Liu, Zhelei Zhou, Yinghao Wang, Yanxin Pang 等USENIX Security 2025
- HERO-Sign: Hierarchical Tuning and Efficient Compiler-Time GPU Optimizations for SPHINCS+ Signature GenerationYaoyun Zhou, Qian WangHPCA 2026
相关 Paper
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