A Run-Time Adaptive FPGA Architecture for Monte Carlo Simulations

Field Programmable Gate Arrays (FPGAs) are now considered to be one of the preferred computing platforms for high performance computing applications, such as Monte Carlo simulations, due to their large computational power and low power consumption. Unlike other state-of-the-art computing platforms, such as General Purpose Processors (GPPs) and General Purpose Graphics Processing Units (GPGPU), FPGAs can moreover exploit the applications´ requirements with respect to the employed number representation scheme, with the potential to lead to considerable area savings and throughput increases. This work proposes a novel FPGA based architecture for Monte Carlo simulations that monitors and configures the number representation of the system during run-time in order to accommodate the dynamics of the system under investigation, resulting to a considerable boost on the overall performance of the system compared to a conventional system. In order to evaluate the efficacy of the proposed architecture, the GARCH model from the financial industry is considered as a case study. The results demonstrate that an average of ~1.35× throughput per resource unit improvement is achieved compared to conventional parallel arithmetic implementation.