Print Email Facebook Twitter ExaFlexHH Title ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations Author Miedema, Rene (Erasmus MC) Strydis, C. (TU Delft Computer Engineering; Erasmus MC) Date 2024 Abstract IntroductionIn-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding 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 performance 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.MethodsThis 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 parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH.ResultsModel 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. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms.DiscussionThe ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations. Subject brain simulationFPGAdataflow enginesystolic arrayscalableInferior OliveHodgkin-HuxleyNeuroML To reference this document use: http://resolver.tudelft.nl/uuid:92bb22bb-a2a2-4291-b5ac-a6d0a8290896 DOI https://doi.org/10.3389/fninf.2024.1330875 ISSN 1662-5196 Source Frontiers in Neuroinformatics, 18 Part of collection Institutional Repository Document type journal article Rights © 2024 Rene Miedema, C. Strydis Files PDF fninf-18-1330875.pdf 3.19 MB Close viewer /islandora/object/uuid:92bb22bb-a2a2-4291-b5ac-a6d0a8290896/datastream/OBJ/view