Falic: An FPGA-Based Multi-Scalar Multiplication Accelerator for Zero-Knowledge Proof

In this paper, we propose Falic, a novel FPGA-based accelerator to accelerate multi-scalar multiplication (MSM), the most time-consuming phase of zk-SNARK proof generation. Falic innovates three techniques. First, it leverages globally asynchronous locally synchronous (GALS) strategy to build multiple small and lightweight MSM cores to parallelize the independent inner product computation on different portions of the scalar vector and point vector. Second, each MSM core contains just one large-integer modular multiplier (LIMM) that is multiplexed to perform the point additions (PADDs) generated during MSM. We strike a balance between the throughput and hardware cost by batching the appropriate number of PADDs and selecting the computation graph of PADD with proper parallelism degree. Finally, the performance is further improved by a simple cache structure that enables the computation reuse. We implement Falic on two different FPGAs with different hardware resources, i.e., the Xilinx U200 and Xilinx U250. Compared to the prior FPGA-based accelerator, Falic improves the MSM throughput by <inline-formula><tex-math notation="LaTeX">$3.9\boldsymbol{\times}$</tex-math></inline-formula>. Experimental results also show that Falic achieves a throughput speedup of up to <inline-formula><tex-math notation="LaTeX">$1.62\boldsymbol{\times}$</tex-math></inline-formula> and saves as much as <inline-formula><tex-math notation="LaTeX">$8.5\boldsymbol{\times}$</tex-math></inline-formula> energy compared to an RTX 2080Ti GPU.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Computers
Published
2024-08-23
DOI
https://doi.org/10.1109/tc.2024.3449121
Citations
3
Primary Topic
Cryptography and Residue Arithmetic
Type
article
Field-Weighted Citation Impact
2.34

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Falic: An FPGA-Based Multi-Scalar Multiplication Accelerator for Zero-Knowledge Proof

Xuehai Qian, Zhibin Yu, Yongkui Yang, Zhenyan Lu et al.
3 citations
IEEE Transactions on Computers
Cryptography and Residue Arithmetic
2.34
article

Falic: An FPGA-Based Multi-Scalar Multiplication Accelerator for Zero-Knowledge Proof

Xuehai Qian, Zhibin Yu, Yongkui Yang, Zhenyan Lu, Jingwei Zeng, Xingguo Liu
article en
3 citations

Abstract

In this paper, we propose Falic, a novel FPGA-based accelerator to accelerate multi-scalar multiplication (MSM), the most time-consuming phase of zk-SNARK proof generation. Falic innovates three techniques. First, it leverages globally asynchronous locally synchronous (GALS) strategy to build multiple small and lightweight MSM cores to parallelize the independent inner product computation on different portions of the scalar vector and point vector. Second, each MSM core contains just one large-integer modular multiplier (LIMM) that is multiplexed to perform the point additions (PADDs) generated during MSM. We strike a balance between the throughput and hardware cost by batching the appropriate number of PADDs and selecting the computation graph of PADD with proper parallelism degree. Finally, the performance is further improved by a simple cache structure that enables the computation reuse. We implement Falic on two different FPGAs with different hardware resources, i.e., the Xilinx U200 and Xilinx U250. Compared to the prior FPGA-based accelerator, Falic improves the MSM throughput by <inline-formula><tex-math notation="LaTeX">$3.9\boldsymbol{\times}$</tex-math></inline-formula>. Experimental results also show that Falic achieves a throughput speedup of up to <inline-formula><tex-math notation="LaTeX">$1.62\boldsymbol{\times}$</tex-math></inline-formula> and saves as much as <inline-formula><tex-math notation="LaTeX">$8.5\boldsymbol{\times}$</tex-math></inline-formula> energy compared to an RTX 2080Ti GPU.

IEEE Transactions on ComputersVol. 73(12)
Beijing University of Posts and Telecommunications (CN), Chinese Academy of Sciences (CN), Shenzhen Institutes of Advanced Technology (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 9%
Cryptography and Residue Arithmetic
2.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.