Chiplet-Based Techniques for Scalable and Memory-Aware Multiscalar Multiplication on Hardware Platforms
Chiplet-Based Techniques for Scalable and Memory-Aware Multiscalar Multiplication on Hardware Platforms
Abstract
This article presents a high-performance architecture for accelerating multiscalar multiplication (MSM) on ASIC platforms, targeting cryptographic applications with high throughput and scalability demands. Current MSM accelerators on FPGA and ASIC platforms typically focus on designing efficient processing elements (PEs) to perform resource-intensive elliptic curve point operations, which require a high number of 384-bit modular multipliers. Our approach diverges from existing works by adopting a chiplet-based design, which optimally balances area, power consumption, and computational throughput. By analyzing memory requirements across window sizes, we determine an optimal mixed configuration of 12- and 13-bit windows, which allows efficient integration of multiple PEs per chiplet. Considering the single-PE case, our design achieves a 1.37x speedup and a 1.3x area reduction over prior works. Moreover, our multi-PE chiplet design outperforms monolithic designs by 2.2x in area–time product while allowing lower production costs and higher yield.
Thank you for visiting this project page. Register this project to explore its novelty and implementation support.
