Pipelined FPGA implementation of a Differential Evolution Engine for Optimization of Scientific Models

Custom computing machines implemented on FPGAs have emerged as a powerful solution for tackling computationally intensive tasks, leveraging their capacity for deep pipelining and parallel memory access. Differential Evolution (DE), a robust optimization algorithm, combined with adaptive numerical integration methods, is widely used to optimize parameter values in diverse scientific models. These tasks involve extensive floating-point computations, making FPGAs an ideal platform for efficiently accelerating their execution. In this work, we present a flexible and scalable FPGA architecture optimized for Differential Evolution. This architecture is tailored to solve complex, resource-intensive optimization problems and is easily customizable for various models and integration methods. To demonstrate its efficacy, we evaluate two case studies: The Hodgkin-Huxley model for neuron action potentials and the Circadian clock model of Arabidopsis thaliana . Our architecture integrates adaptive numerical methods with DE and achieves significant performance and energy efficiency gains over CPU and GPU implementations while maintaining versatility across applications. Our architecture's modular design enables seamless adaptation across different scientific contexts, enabling further optimization of resource utilization and expansion of application domains. The results underline the potential of FPGAs as a superior platform for large-scale scientific computation, offering unmatched energy efficiency and computational throughput for highly demanding tasks. The code developed to carry out this work is publicly available at https://github.com/mdccUVa/de-fpga .