Spiking neural networks and neuromorphic hardware platforms that emulate neural dynamics are slowly gaining momentum and entering main-stream usage. Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms. Correspondingly, there are a plethora of software and hardware implementations with their own unique technology stacks. Consequently, neuromorphic systems typically diverge from the expected computational model, which challenges the reproducibility and reliability across platforms. Additionally, most neuromorphic hardware is limited by its access via a single software frameworks with a limited set of training procedures. Here, we establish a common reference-frame for computations in neuromorphic systems, dubbed the Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational primitives as idealized continuous-time hybrid systems that can be composed into graphs and mapped to and from various neuromorphic technology stacks. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the fundamental computation, while simultaneously exposing the exact differences between the evaluated implementation and the idealized mathematical formalism. We reproduce three NIR graphs across 7 neuromorphic simulators and 4 hardware platforms, demonstrating support for an unprecedented number of neuromorphic systems. With NIR, we decouple the evolution of neuromorphic hardware and software, ultimately increasing the interoperability between platforms and improving accessibility to neuromorphic technologies. We believe that NIR is an important step towards the continued study of brain-inspired hardware and bottom-up approaches aimed at an improved understanding of the computational underpinnings of nervous systems.

翻译：脉冲神经网络及模拟神经动力学的神经形态硬件平台正逐渐崭露头角并进入主流应用。尽管神经动力学已具备完善的数学基础，但不同平台的实现细节差异显著。相应地，众多软硬件实现方案各自拥有独特的技术栈，导致神经形态系统通常偏离预期的计算模型，跨平台的可重现性与可靠性面临挑战。此外，多数神经形态硬件受限于单一软件框架及有限的训练流程。为此，我们建立了神经形态计算的通用参考框架——神经形态中间表示（NIR）。NIR定义了一组计算原语，以理想化的连续时间混合系统形式呈现，这些原语可组合为计算图，并能映射至各类神经形态技术栈（或从技术栈反向映射）。通过抽象化离散化与硬件约束等假设，NIR既忠实保留了基础计算过程，又清晰揭示了实际实现与理想化数学形式之间的精确差异。我们在7个神经形态模拟器与4个硬件平台上复现了三个NIR计算图，验证了对前所未有数量的神经形态系统的支持能力。借助NIR，我们解耦了神经形态硬件与软件的演进过程，最终提升了平台间的互操作性，并增进了神经形态技术的可及性。我们坚信，NIR是推动脑启发硬件持续研究、以及通过自下而上方法深化对神经系统计算基础认知的重要一步。