ReZK: A Highly Reconfigurable Accelerator for Zero-Knowledge Proof

Zero-knowledge proof (ZKP) plays a significant role in privacy protection technology. However, the proof generation phase requires considerable time and hardware resources. In this phase, Number Theoretic Transform or Inverse Number Theoretic Transform (NTT/INTT) in polynomial computation, as well as Multiple Scalar Multiplication (MSM), are bottlenecks that dominate the execution time. In this paper, we propose a highly reconfigurable accelerator ReZK to accelerate ZKP proof generation phase, focusing on NTT/INTT and MSM. According to the configurations, ReZK can be configured as NTT, INTT, and MSM with variable sizes and bit-widths by adjusting the data path between on-chip memories and arithmetic cores. As the basic unit of arithmetic cores, the reconfigurable processing element (PE) in ReZK is composed of pipelined modular multipliers and modular adders that support variable bit-widths. It can perform butterfly or arithmetic operations. Based on the reconfigurable PEs, the ReZK core can implement NTT/INTT with different sizes and bit-widths, or a fully pipelined point adder (PADD). Additionally, we propose a modularized MSM scheduling architecture to support various bit-widths. The on-chip memories are also well organized for reuse. In NTT/INTT mode, 4-way 256-bit or 2-way 384-bit NTT/INTT can be computed in parallel. In MSM mode, for different elliptic curves, ReZK is capable of processing 4-way 256-bit or 2-way 384-bit MSM in parallel.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2024-10-11
DOI
https://doi.org/10.1109/tcsi.2024.3468033
Citations
5
Primary Topic
Security and Verification in Computing
Type
article
Field-Weighted Citation Impact
1.74

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article

ReZK: A Highly Reconfigurable Accelerator for Zero-Knowledge Proof

Li Shang, Changxu Liu, Fan Yang, Hao Zhou et al.
5 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Security and Verification in Computing
1.74
article

ReZK: A Highly Reconfigurable Accelerator for Zero-Knowledge Proof

Li Shang, Changxu Liu, Fan Yang, Hao Zhou, Lan Yang
article en
5 citations

Abstract

Zero-knowledge proof (ZKP) plays a significant role in privacy protection technology. However, the proof generation phase requires considerable time and hardware resources. In this phase, Number Theoretic Transform or Inverse Number Theoretic Transform (NTT/INTT) in polynomial computation, as well as Multiple Scalar Multiplication (MSM), are bottlenecks that dominate the execution time. In this paper, we propose a highly reconfigurable accelerator ReZK to accelerate ZKP proof generation phase, focusing on NTT/INTT and MSM. According to the configurations, ReZK can be configured as NTT, INTT, and MSM with variable sizes and bit-widths by adjusting the data path between on-chip memories and arithmetic cores. As the basic unit of arithmetic cores, the reconfigurable processing element (PE) in ReZK is composed of pipelined modular multipliers and modular adders that support variable bit-widths. It can perform butterfly or arithmetic operations. Based on the reconfigurable PEs, the ReZK core can implement NTT/INTT with different sizes and bit-widths, or a fully pipelined point adder (PADD). Additionally, we propose a modularized MSM scheduling architecture to support various bit-widths. The on-chip memories are also well organized for reuse. In NTT/INTT mode, 4-way 256-bit or 2-way 384-bit NTT/INTT can be computed in parallel. In MSM mode, for different elliptic curves, ReZK is capable of processing 4-way 256-bit or 2-way 384-bit MSM in parallel.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(2)
Fudan University (CN), Shanghai Fudan Microelectronics (China) (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 13%
Security and Verification in Computing
1.74
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ReZK: A Highly Reconfigurable Accelerator for Zero-Knowledge Proof — Li Shang, Changxu Liu, et al. · IEEE Transactions on Circuits and Systems I Regular Papers (2024) | TGRS Research Map | TGRS