Accelerating Zero-Knowledge Proofs Through Hardware-Algorithm Co-Design

Zero-Knowledge Proofs (ZKPs) are a cryptographic tool that enables one party (a prover) to prove to another (a verifier) that a statement is true, without requiring the prover to disclose any data to the verifier. ZKPs have many use cases, such as letting clients delegate computation to servers with cryptographic correctness guarantees, while enabling the server to use secret data in these computations. ZKP applications span verifiable machine learning (ML) and databases, online auctions, electronic voting, and blockchains. While ZKPs are already widely used in blockchains, the prohibitive costs of proof generation limit them to proving very simple computations. We present a novel accelerator, NoCap, that leverages hardware-algorithm co-design to achieve transformative speedups. NoCap generates proofs 586× faster than a 32-core CPU, and 41× faster than PipeZK, a state-of-the-art ZKP accelerator. We leverage recent algorithmic developments to achieve these speedups: we identify and combine two recent hash-based ZKP algorithms, Orion and Spartan, which have similar performance on CPUs to the ZKPs targeted by prior accelerators, but are much more amenable to hardware acceleration. Though these algorithms result in larger proofs, we show that the end-to-end speedups (including prover time, proof transmission, and verification time) more than justify this size increase. We contribute a novel hardware organization to exploit these acceleration opportunities: NoCap is a programmable vector processor with functional units tailored to the needs of hash-based ZKPs. We also contribute a co-designed implementation of the Spartan+Orion ZKP tailored to accelerators, with optimizations that improve parallelism and reduce memory traffic. As a result, NoCap achieves speedups that enable new use cases for ZKP.

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Publication Details

Published
2024-11-02
DOI
https://doi.org/10.1109/micro61859.2024.00035
Citations
14
Primary Topic
Numerical Methods and Algorithms
Type
article
Field-Weighted Citation Impact
5.06

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article

Accelerating Zero-Knowledge Proofs Through Hardware-Algorithm Co-Design

Srinivas Devadas, Daniel Sánchez, Simon Langowski, Nikola Samardzic
14 citations
Numerical Methods and Algorithms
5.06
article

Accelerating Zero-Knowledge Proofs Through Hardware-Algorithm Co-Design

Srinivas Devadas, Daniel Sánchez, Simon Langowski, Nikola Samardzic
article en
14 citations

Abstract

Zero-Knowledge Proofs (ZKPs) are a cryptographic tool that enables one party (a prover) to prove to another (a verifier) that a statement is true, without requiring the prover to disclose any data to the verifier. ZKPs have many use cases, such as letting clients delegate computation to servers with cryptographic correctness guarantees, while enabling the server to use secret data in these computations. ZKP applications span verifiable machine learning (ML) and databases, online auctions, electronic voting, and blockchains. While ZKPs are already widely used in blockchains, the prohibitive costs of proof generation limit them to proving very simple computations. We present a novel accelerator, NoCap, that leverages hardware-algorithm co-design to achieve transformative speedups. NoCap generates proofs 586× faster than a 32-core CPU, and 41× faster than PipeZK, a state-of-the-art ZKP accelerator. We leverage recent algorithmic developments to achieve these speedups: we identify and combine two recent hash-based ZKP algorithms, Orion and Spartan, which have similar performance on CPUs to the ZKPs targeted by prior accelerators, but are much more amenable to hardware acceleration. Though these algorithms result in larger proofs, we show that the end-to-end speedups (including prover time, proof transmission, and verification time) more than justify this size increase. We contribute a novel hardware organization to exploit these acceleration opportunities: NoCap is a programmable vector processor with functional units tailored to the needs of hash-based ZKPs. We also contribute a co-designed implementation of the Spartan+Orion ZKP tailored to accelerators, with optimizations that improve parallelism and reduce memory traffic. As a result, NoCap achieves speedups that enable new use cases for ZKP.

National Science Foundation
Openalex Percentile: Top 4%
Numerical Methods and Algorithms
5.06
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