Yoimiya: A Scalable Framework for Optimal Resource Utilization in ZK-SNARK Systems

TL;DR

Yoimiya is a scalable framework that enhances resource utilization and speeds up proof generation in ZK-SNARK systems by automatic circuit partitioning and decoupling witness generation from proof computation.

Contribution

It introduces an automatic circuit partitioning algorithm and decouples witness generation from proof computation for improved efficiency in ZK-SNARKs.

Findings

Significantly improves proof generation speed.

Enhances resource utilization through parallel processing.

Effectively balances the two phases for optimal performance.

Abstract

With the widespread adoption of Zero-Knowledge Proof systems, particularly ZK-SNARK, the efficiency of proof generation, encompassing both the witness generation and proof computation phases, has become a significant concern. While substantial efforts have successfully accelerated proof computation, progress in optimizing witness generation remains limited, which inevitably hampers overall efficiency. In this paper, we propose Yoimiya, a scalable framework with pipeline, to optimize the efficiency in ZK-SNARK systems. First, Yoimiya introduces an automatic circuit partitioning algorithm that divides large circuits of ZK-SNARK into smaller subcircuits, the minimal computing units with smaller memory requirement, allowing parallel processing on multiple units. Second, Yoimiya decouples witness generation from proof computation, and achieves simultaneous executions over units from multiple circuits. Moreover, Yoimiya enables each phase scalable separately by configuring the resource distribution to make the time costs of the two phases aligned, maximizing the resource utilization. Experimental results confirmed that our framework effectively improves the resource utilization and proof generation speed.