Reverse longstaff-schwartz american option pricing on hybrid CPU/FPGA systems

In today's markets, high-speed and energy-efficient computations are mandatory in the financial and insurance industry. At the same time, the gradual convergence of highperformance computing with embedded systems is having a huge impact on the design methodologies, where dedicated accelerators are implemented to increase performance and energy efficiency. This paper follows this trend and presents a novel way to price high-dimensional American options using techniques of the embedded community. The proposed architecture targets heterogeneous CPU/FPGA systems, and it exploits the FPGA reconfiguration to deliver high-throughput. With a bit-true algorithmic transformation based on recomputation, it is possible to eliminate the memory bottleneck and access costs. The result is a pricing system that is 16x faster and 268x more energy-efficient than an optimized Intel CPU implementation.