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A system hierarchy for brain-inspired computing.

Youhui Zhang1,2,3, Peng Qu4,5,6, Yu Ji4,5,6

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Neuromorphic computing lacks a universal system hierarchy. This study introduces 'neuromorphic completeness' and a system hierarchy to ensure programming-language portability and hardware compatibility for brain-inspired AI development.

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Neuroscience

Background:

  • Neuromorphic computing, inspired by the brain, offers potential for next-gen computer engineering and artificial general intelligence.
  • Current brain-inspired computing lacks a generalized system hierarchy and understanding of completeness, hindering software-hardware compatibility and development productivity.

Purpose of the Study:

  • To propose 'neuromorphic completeness' and a corresponding system hierarchy for brain-inspired computing.
  • To address the lack of programming-language portability and hardware compatibility in current neuromorphic systems.

Main Methods:

  • Introduced 'neuromorphic completeness,' relaxing hardware completeness requirements.
  • Developed a system hierarchy with a Turing-complete software-abstraction model and a versatile abstract neuromorphic architecture.
  • Implemented toolchain software for program execution across diverse neuromorphic hardware platforms.

Main Results:

  • Established a system hierarchy enabling uniform program representation and transformation for neuromorphic complete hardware.
  • Demonstrated programming-language portability, hardware completeness, and compilation feasibility.
  • Introduced a new system-design dimension through neuromorphic completeness.

Conclusions:

  • The proposed system hierarchy and neuromorphic completeness facilitate efficient and compatible progress in brain-inspired computing.
  • This framework is expected to accelerate the development of various applications, including artificial general intelligence.