Neuromorphic computing draws inspiration from the brain to provide computing technology and architecture with the potential to drive the next wave of computer engineering1-13. Such brain-inspired computing also provides a promising platform for the development of artificial general intelligence14,15. However, unlike conventional computing systems, which have a well established computer hierarchy built around the concept of Turing completeness and the von Neumann architecture16-18, there is currently no generalized system hierarchy or understanding of completeness for brain-inspired computing. This affects the compatibility between software and hardware, impairing the programming flexibility and development productivity of brain-inspired computing. Here we propose 'neuromorphic completeness', which relaxes the requirement for hardware completeness, and a corresponding system hierarchy, which consists of a Turing-complete software-abstraction model and a versatile abstract neuromorphic architecture. Using this hierarchy, various programs can be described as uniform representations and transformed into the equivalent executable on any neuromorphic complete hardware-that is, it ensures programming-language portability, hardware completeness and compilation feasibility. We implement toolchain software to support the execution of different types of program on various typical hardware platforms, demonstrating the advantage of our system hierarchy, including a new system-design dimension introduced by the neuromorphic completeness. We expect that our study will enable efficient and compatible progress in all aspects of brain-inspired computing systems, facilitating the development of various applications, including artificial general intelligence.

中文翻译：

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类脑计算的系统层次结构

神经拟态计算从大脑中汲取灵感，提供具有推动下一波计算机工程浪潮潜力的计算技术和架构1-13。这种类脑计算也为通用人工智能的发展提供了一个有前景的平台14,15。然而，与具有围绕图灵完备性概念和冯诺依曼架构 16-18 建立的完善计算机层次结构的传统计算系统不同，目前没有通用的系统层次结构或对类脑计算的完整性的理解。这会影响软件和硬件之间的兼容性，损害类脑计算的编程灵活性和开发生产力。在这里，我们提出了“神经形态完整性”，它放宽了对硬件完整性的要求，以及相应的系统层次结构，它由图灵完备的软件抽象模型和通用的抽象神经形态架构组成。使用这种层次结构，各种程序可以被描述为统一的表示，并在任何神经形态的完整硬件上转换为等效的可执行文件——也就是说，它确保了编程语言的可移植性、硬件完整性和编译可行性。我们实施工具链软件以支持在各种典型硬件平台上执行不同类型的程序，展示了我们系统层次结构的优势，包括神经形态完整性引入的新系统设计维度。