Abstract:
OpenCMISS is a mathematical modeling environment designed to solve field based equations and link subcellular and tissue-level biophysical processes to organ-level processes. It employs a general purpose parallel design, in particular distributed memory, for its computations. CellML is a mark up language based on XML that is designed to encode lumped parameter biophysically based systems of ordinary differential equations and nonlinear algebraic equations. OpenCMISS allows CellML models to be evaluated and integrated into models at various spatial and temporal scales. With good inherent parallelism, hardware acceleration based on FPGAs has a great potential to increase the computational performance and to reduce the energy consumption of computations with CellML models integrated in OpenCMISS. However, with several hundred CellML models, manual hardware implementation for each CellML model is complex and time consuming. The advantages of FPGA designs will only be realised if there is a general solution or a tool to automatically convert CellML models into hardware description languages such as VHDL. In this paper we describe the architecture for the FPGA hardware implementation of CellML models and evaluate the first results related to performance and resource usage based on a variety of criteria.
