if-ZKP: Intel FPGA-Based Acceleration of Zero Knowledge Proofs

TL;DR

This paper introduces an FPGA-based architecture that significantly accelerates zk-SNARK proof generation, especially multi-scalar multiplication, achieving over 100x speedup and supporting popular elliptic curves.

Contribution

The paper presents the first FPGA hardware acceleration for zk-SNARKs, optimizing multi-scalar multiplication using Intel IP Libraries and the OneAPI framework.

Findings

Achieved 110x-150x speedup over software implementations.

Supports BLS12-381 and BN128 elliptic curves on FPGA.

Demonstrated scalable and efficient zk-SNARK prover acceleration.

Abstract

Zero-Knowledge Proofs (ZKPs) have emerged as an important cryptographic technique allowing one party (prover) to prove the correctness of a statement to some other party (verifier) and nothing else. ZKPs give rise to user's privacy in many applications such as blockchains, digital voting, and machine learning. Traditionally, ZKPs suffered from poor scalability but recently, a sub-class of ZKPs known as Zero-knowledge Succinct Non-interactive ARgument of Knowledges (zk-SNARKs) have addressed this challenge. They are getting significant attention and are being implemented by many public libraries. In this paper, we present a novel scalable architecture that is suitable for accelerating the zk-SNARK prover compute on FPGAs. We focus on the multi-scalar multiplication (MSM) that accounts for the majority of computation time spent in zk-SNARK systems. The MSM calculations extensive rely on modular arithmetic so highly optimized Intel IP Libraries for modular arithmetic are used. The proposed architecture exploits the parallelism inherent to MSM and is implemented using the Intel OneAPI framework for FPGAs. Our implementation runs 110x-150x faster compared to reference software library, uses a generic curve form in Jacobian coordinates and is the first to report FPGA hardware acceleration results for BLS12-381 and BN128 family of elliptic curves.