ODoST: Automatic Hardware Acceleration for Biomedical Model Integration

Abstract

Dynamic biomedical systems are mathematically described by Ordinary Differential Equations (ODEs) and their solution is often one of the most computationally intensive parts in biomedical simulations. With high inherent parallelism, hardware acceleration based on Field-Programmable Gate Arrays (FPGAs) has great potential to increase the computational performance of the model simulations, while being very power-efficient. However, the manual hardware implementation is complex and time consuming. The advantages of FPGA designs can only be realised if there is a general solution to automate the process. In this article, we propose a domain-specific high-level synthesis tool called ODoST that automatically generates an FPGA-based Hardware Accelerator Module (HAM) from a high-level description. In this direct approach, ODE equations are directly mapped to processing pipelines without any intermediate architecture layer of processing elements. We evaluate the generated HAMs on real hardware based on their resource usage, processing speed, and power consumption, and compare them with CPUs and a GPU. The results show that FPGA implementations can achieve 15.3 times more speedup compared to a single core CPU solution and perform similarly to an auto-generated GPU solution, while the FPGA implementations can achieve 14.5 times more power efficiency than the CPU and 3.1 times compared to the optimised GPU solution. Improved speedups are foreseeable based on further optimisations.