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.

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ExaFlexHH：一个可用于百亿亿次计算的灵活多 FPGA 库，用于生物学上合理的大脑模拟

介绍计算机模拟模拟是现代神经科学的一个强大工具，可以增强我们对不同生理水平上复杂大脑系统的理解。为了对生物真实且详细的系统进行建模，理想的仿真平台必须具备：（1）高性能和性能可扩展性，（2）灵活性，以及​​（3）非技术用户的易用性。然而，大多数现有平台和库并不满足所有三个标准，特别是对于 Hodgkin-Huxley (HH) 模型等复杂模型或间隙连接等复杂神经元连接建模。方法这项工作介绍ExaFlexHH，一个支持百亿亿次计算的灵活库，用于在多 FPGA 平台上模拟 HH 模型。 ExaFlexHH 利用基于 FPGA 的数据流引擎 (DFE) 和数据流编程范例，满足所有三个要求。该库还可参数化并符合 NeuroML（计算神经科学中一种著名的大脑描述语言）。我们通过使用 ExaFlexHH 实现要求很高的 Inferior Olive 扩展 Hodgkin-Huxley (eHH) 模型来展示该平台的性能可扩展性。结果模型仿真结果显示了未连接网络的线性可扩展性和具有复杂突触可塑性的网络的近线性可扩展性，与单个 FPGA 模拟相比，使用两个 FPGA 的性能提高了 1.99 倍，而在可扩展环形拓扑中使用八个 FPGA 时，性能提高了 7.96 倍。值得注意的是，我们的结果还揭示了每瓦 GFLOPS 的一致性能效率，进一步促进了百亿亿级计算速度并突破了未来大脑模拟平台的界限。讨论 ExaFlexHH 库显示出卓越的资源效率，以每硬件资源的 FLOPS 进行量化，并与其他库进行了基准测试基于 FPGA 的具有竞争力的大脑模拟实现。