ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (6): 1146-1154.doi: 10.7544/issn1000-1239.2021.20210170

所属专题: 2021计算机芯片关键技术前沿与进展专题

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  1. 1(清华大学计算机科学与技术系,北京信息科学与技术国家研究中心 北京 100084);2(数学工程与先进计算国家重点实验室 江苏无锡 214125) (
  • 出版日期: 2021-06-01
  • 基金资助: 

A Proposal of Software-Hardware Decoupling Hardware Design Method for Brain-Inspired Computing

Qu Peng1,2, Chen Jiajie1, Zhang Youhui1, Zheng Weimin1   

  1. 1(Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084);2(State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi, Jiangsu 214125)
  • Online: 2021-06-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62050340) and the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (2020A07).

摘要: 类脑计算是一个涉及到多领域、多学科的新兴领域,对计算神经科学、人工智能和新型体系结构设计都具有重要的支持和启发意义.但是类脑计算系统领域发展所面临的重要问题之一是软硬件紧耦合.近期的一项研究提出了神经形态完备性的概念,为实现类脑计算系统领域的软硬件解耦合提供了理论支持,并作为样例研究提出了对应的系统层次结构设计.作为这一工作的后续,首先对神经形态完备性和类脑计算层次结构中部分关键的概念进行了阐述与讨论,之后进一步提出了在这一概念和体系结构设计下,实现支持软硬件解耦合的类脑计算硬件设计方法的构想,即由执行原语集合设计以及硬件实现方法设计组成的迭代调整的设计流程.最后,展示了正在进行的基于FPGA的相应评估平台工作.这一硬件设计方法有助于实现神经形态完备的高效原语集合和芯片设计,从而有利于实现类脑计算系统领域的软硬件解耦合.

关键词: 类脑计算, 完备性, 软硬件解耦合, FPGA, 性能评估

Abstract: Brain-inspired computing is a novel research field involving multiple disciplines, which may have important implications for the development of computational neuroscience, artificial intelligence, and computer architectures. Currently, one of the key problems in this field is that brain-inspired software and hardware are usually tightly coupled. A recent study has proposed the notion of neuromorphic completeness and the corresponding system hierarchy design. This completeness provides a theoretical support for realizing the decoupling of hardware and software of brain-inspired computing systems, and the system hierarchy design can be viewed as a reference implementation of neuromorphic complete software and hardware. As a position paper, this article first discusses several key concepts of neuromorphic completeness and the system hierarchy for brain-inspired computing. Then, as a follow-up work, we propose a design method for software-hardware decoupling hardware design of brain-inspired computing, namely, an iterative optimization process consisting of execution primitive set design and hardware implementation evaluation. Finally, we show the preliminary status of our research on the FPGA based evaluation platform. We believe that this method would contribute to the realization of extensible, neuromorphic complete computation primitive sets and chips, which is beneficial to realize the decoupling of hardware and software in the field of brain-inspired computing systems.

Key words: brain-inspired computing, completeness, software-hardware decoupling, FPGA, performance evaluation