ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations - Miedema, Rene; Strydis, Christos (Frontiers in Neuroinformatics, 2024)
In-silico simulations are a powerful tool in modern neuroscience for enhancing our understand- ing of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: 1) high performance and perfor- mance scalability, 2) flexibility, and 3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions. This work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also param- eterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. As an example, we demonstrate the performance, portability, and maintainability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH. Model simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99× performance increase using two FPGAs compared to a single FPGA simulation, and 7.96× when using eight FPGAs in a scalable, ring topology.