PriorMSM: An Efficient Acceleration Architecture for Multi-Scalar Multiplication

Multi-Scalar Multiplication (MSM) is a computationally intensive task that operates on elliptic curves based on GF ( P ). It is commonly used in Zero-knowledge proof (ZKP), where it accounts for a significant portion of the computation time required for proof generation. In this paper, we present PriorMSM, an efficient acceleration architecture for MSM. We propose a Priority-based Scheduling Mechanism (PBSM) based on a multi-FIFOs and multi-banks architecture to accelerate the implementation of MSM. By increasing the pairing success rate of internal points, PBSM reduces the number of bubbles in the pipeline of point addition (PADD), consequently improving the data throughput of the pipeline. We also introduce an advanced parallel bucket aggregation algorithm, leveraging PADD’s fully pipelined characteristics to significantly accelerate the implementation of bucket aggregation. We perform a sensitivity analysis on the crucial parameter, window size, in MSM. The results indicate that the window size of the MSM significantly impacts its latency. Area-Time Product (ATP) metric is introduced to guide the selection of the optimal window size, balancing the performance and cost for practical applications of subsequent MSM implementations. PriorMSM is evaluated using the TSMC 28nm process. It achieves a maximum speedup of 10.9 × compared to the previous custom hardware implementations and a maximum speedup of 3.9 × compared to the GPU implementations.