Performance optimisation strategies for automatically generated FPGA accelerators for biomedical models

Abstract

Biomedical modelling that is mathematically described by ordinary differential equations (ODEs) is often one of the most computationally intensive parts of simulations. With high inherent parallelism, hardware acceleration based on field programmable gate array has great potential to increase the computational performance of the ODE model integration while being very power efficient. ODE-based Domain-specific Synthesis Tool is a tool we proposed previously to automatically generate the complete hardware/software co-design framework for computing biomedical models based on CellML. Although it provides remarkable performance improvement and high energy efficiency compared with CPUs and GPUs, there is still a great potential for optimisation. In this paper, we investigate a set of optimisation strategies including compiler optimisation, resource fitting and balancing, and multiple pipelines. They all have in common that they can be performed automatically and hence can be integrated in our domain-specific high level synthesis tool. We evaluate the optimised hardware accelerator modules generated by ODE-based Domain-specific Synthesis Tool on real hardware based on their resource usage, processing speed and power consumption. The results are compared with single threaded and multi-core CPUs with/without Streaming SIMD Extension (SSE) optimisation and a graphics card. The results show that the proposed optimisation strategies provide significant performance improvement and result in even more energy-efficient hardware accelerator modules. Furthermore, the resources of the target field programmable gate array device can be more efficiently utilised in order to fit larger biomedical models than before.