KTH Royal Institute of Technology, Stockholm, Sweden.
CogniGron Center, University of Groningen, Groningen, Netherlands.
Nat Commun. 2024 Sep 16;15(1):8122. doi: 10.1038/s41467-024-52259-9.
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org.
尖峰神经网络和模拟神经元动力学的神经形态硬件平台越来越受到关注，并被广泛应用于机器学习相关的许多问题中。尽管神经动力学有一个成熟的数学基础，但仍存在许多软件和硬件解决方案和堆栈，它们的多样性使得难以重现研究结果。在这里，我们为数字神经形态系统中的计算建立了一个通用的参考框架，称为神经形态中间表示（NIR）。NIR 定义了一组计算和可组合的模型原语，作为结合连续时间动力学和离散事件的混合系统。通过抽象离散化和硬件约束的假设，NIR 忠实地捕捉了计算模型，同时弥合了评估的实现与基础数学形式主义之间的差异。NIR 支持数量空前的神经形态系统，我们通过在 7 个神经形态模拟器和 4 个数字硬件平台上复制 3 个具有不同复杂度的尖峰神经网络模型来证明这一点。NIR 分离了神经形态硬件和软件的开发，实现了平台之间的互操作性，并提高了对多种神经形态技术的访问。我们相信，NIR 是脑启发式软硬件协同进化的关键下一步，它为实现神经系统的节能计算原理的研究铺平了道路。NIR 可在 neuroir.org 获得。
