# SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs

arXiv: https://arxiv.org/abs/2408.05890 · HTML: https://mlsanity.com/paper/2408.05890/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [A Survey on Privacy Risks and Protection in Large Language Models](https://mlsanity.com/paper/2505.01976/) (2025)
- [Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications](https://mlsanity.com/paper/2509.25072/) (2025)

## Abstract (arXiv)

Zero-Knowledge Proofs (ZKPs) are an emergent paradigm in verifiable computing. In the context of applications like cloud computing, ZKPs can be used by a client (called the verifier) to verify the service provider (called the prover) is in fact performing the correct computation based on a public input. A recently prominent variant of ZKPs is zkSNARKs, generating succinct proofs that can be rapidly verified by the end user. However, proof generation itself is very time consuming per transaction. Two key primitives in proof generation are the Number Theoretic Transform (NTT) and Multi-scalar Multiplication (MSM). These primitives are prime candidates for hardware acceleration, and prior works have looked at GPU implementations and custom RTL. However, both algorithms involve complex dataflow patterns -- standard NTTs have irregular memory accesses for butterfly computations from stage to stage, and MSMs using Pippenger's algorithm have data-dependent memory accesses for partial sum calculations. We present SZKP, a scalable accelerator framework that is the first ASIC to accelerate an entire proof on-chip by leveraging structured dataflows for both NTTs and MSMs. SZKP achieves conservative full-proof speedups of over 400×, 3×, and 12× over CPU, ASIC, and GPU implementations.