Paper 2025/620 Original: eprint.iacr.org/2025/620 ↗

Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge Proofs

Abstract

Zero-Knowledge Proofs (ZKPs) are a rapidly growing technique for privacy-preserving and verifiable computation. ZKPs enable one party (a prover: $\mathcal{P}$) to prove to another (a verifier: $\mathcal{V}$) that a statement is true or correct without revealing any additional information. This powerful capability has led to ZKPs being applied and proposed for application in blockchain technologies, verifiable machine learning, and electronic voting. However, ZKPs have yet to see widespread, ubiquitous adoption due to the exceptionally high computational complexity of the proving process. Naturally, there has been recent work to accelerate ZKP primitives and protocols using GPUs and ASICs. However, the protocols considered so far face one of two challenges: they require a trusted setup for each new application or generate large proofs with high verification costs, limiting their applicability in scenarios with numerous verifiers or strict verification time constraints. HyperPlonk is a state-of-the-art ZKP protocol that supports both one-time, universal setup and small proof sizes/verification costs expected by publicly verifiable, consensus-based systems (e.g., blockchain). While HyperPlonk's setup and verifier properties are highly desirable, the proving phase is costly. A HyperPlonk prover must compute on large bitwidths (e.g., 255-381b) and polynomials (e.g., of degree 2$^{24}$), employs computationally (e.g., MSM) and bandwidth (e.g., SumCheck) intensive kernels, and the complete protocol comprises many steps, each constituting distinct kernels. We present an accelerator, \emph{zkSpeed}, to address these challenges and effectively accelerate HyperPlonk. zkSpeed provides hardware support for all major primitives (e.g., SumCheck and Multi-Scalar Multiplications (MSMs)) and judiciously schedules each protocol phase onto the allocated hardware. We leverage high-level synthesis to thoroughly explore and optimize the hardware design tradeoffs of each unit. These are then input into a full-chip simulator for large-scale design space exploration to optimize all aspects of the architecture in unison. Our Pareto analysis demonstrates that with a 366mm$^2$ chip and 2 TB/s of off-chip bandwidth, zkSpeed is able to accelerate the entire proof generation by 801$\times$ (geomean) over a CPU baseline.