Accelerating Scientific Model Optimization with a Pipelined FPGA-Based Differential Evolution Engine

Custom computing machines on FPGAs excel in solving computationally intensive tasks by leveraging deep pipelines and parallel memory access. This paper introduces a flexible FPGA-based architecture for Differential Evolution (DE), optimized for a variety of scientific models and numerical integration methods. The architecture's modular design allows seamless customization for diverse applications. Two case studies demonstrate the architecture's capabilities: the Hodgkin-Huxley model for neuron action potentials and the Circadian model of Arabidopsis thaliana. These implementations employ double-precision floating-point arithmetic and adaptive numerical integration techniques, addressing the challenges of complex, stiff differential equations. The proposed design outperforms CPUs and GPUs in computational speed and energy efficiency, achieving up to 3.8x faster processing and significant reductions in energy consumption. This work highlights the potential of FPGA platforms for accelerating complex scientific computations while providing insights for future optimizations in resource utilization and broader applicability.