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

被引：0
|
作者：
Qu P. [1 ,2 ]
Chen J. [1 ]
Zhang Y. [1 ]
Zheng W. [1 ]
机构：
[1] Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing
[2] State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2021年 / 58卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Brain-inspired computing; Completeness; FPGA; Performance evaluation; Software-hardware decoupling;
D O I：
10.7544/issn1000-1239.2021.20210170
中图分类号：
学科分类号：
摘要：
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. © 2021, Science Press. All right reserved.
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页码：1146 / 1154
页数：8
相关论文
共 33 条
  • [1] Jordan J, Ippen T, Helias M, Et al., Extremely scalable spiking neuronal network simulation code: From laptops to exascale computers, Frontiers in Neuroinformatics, 12, 2, pp. 1-21, (2018)
  • [2] Marblestone A, Wayne G, Kording K, Et al., Toward an integration of deep learning and neuroscience, Frontiers in Computational Neuroscience, 10, 94, pp. 1-41, (2016)
  • [3] Hassabis D, Kumaran D, Summerfield C, Et al., Neuroscience-inspired artificial intelligence, Neuron, 95, 2, pp. 245-258, (2017)
  • [4] Pei Jing, Deng Lei, Song Sen, Et al., Towards artificial general intelligence with hybrid Tianjic chip architecture, Nature, 572, 7767, pp. 106-111, (2019)
  • [5] Roy K, Jaiswal A, Panda P., Towards spike-based machine intelligence with neuromorphic computing, Nature, 575, 7784, pp. 607-617, (2019)
  • [6] Richards B, Lillicrap T, Beaudoin P, Et al., A deep learning framework for neuroscience, Nature Neuroscience, 22, 11, pp. 1761-1770, (2019)
  • [7] Huang Tiejun, Yu Zhaofei, Liu Yijun, Brain-like machine: Thought and architecture, Journal of Computer Research and Development, 56, 6, pp. 1135-1148, (2019)
  • [8] Han Dong, Zhou Shengyuan, Zhi Tian, Et al., A survey of artificial intelligence chip, Journal of Computer Research and Development, 56, 1, pp. 7-22, (2019)
  • [9] Waldrop M., The chips are down for Moore's law, Nature, 530, 7589, pp. 144-147, (2016)
  • [10] Mead C., Neuromorphic electronic systems, Proceedings of the IEEE, 78, 10, pp. 1629-1636, (1990)
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