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Review
. 2022 Nov 24:16:1041108.
doi: 10.3389/fnbot.2022.1041108. eCollection 2022.

An overview of brain-like computing: Architecture, applications, and future trends

Wei Ou  1   2 , Shitao Xiao  3 , Chengyu Zhu  1 , Wenbao Han  1 , Qionglu Zhang  4
Affiliations
Review

An overview of brain-like computing: Architecture, applications, and future trends

Wei Ou et al. Front Neurorobot. .

Abstract

With the development of technology, Moore's law will come to an end, and scientists are trying to find a new way out in brain-like computing. But we still know very little about how the brain works. At the present stage of research, brain-like models are all structured to mimic the brain in order to achieve some of the brain's functions, and then continue to improve the theories and models. This article summarizes the important progress and status of brain-like computing, summarizes the generally accepted and feasible brain-like computing models, introduces, analyzes, and compares the more mature brain-like computing chips, outlines the attempts and challenges of brain-like computing applications at this stage, and looks forward to the future development of brain-like computing. It is hoped that the summarized results will help relevant researchers and practitioners to quickly grasp the research progress in the field of brain-like computing and acquire the application methods and related knowledge in this field.

Keywords: brain-like computing; learning algorithms; neuromorphic chips; neuronal models; spiking neural learning; spiking neuron networks.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The structure of the article is as follows the analysis of relevant models, the establishment of related platforms, implementation of related applications, challenges, and prospects.
Figure 2
Figure 2
Brain-like computing has evolved from conceptual advancement to technical hibernation to accelerated development due to the possible end of Moore's law.
Figure 3
Figure 3
Overall microarchitecture of the Darwin Neural Processing Unit (NPU) and the process of processing AER packages and outputting them.
Figure 4
Figure 4
The unified functional core (Fcore) of the Tianjic chip consists of four main components: axons, dendrites, soma, and router.
Figure 5
Figure 5
Design of the Tianjic chip and its specific processing flow in ANN and SNN mode.
Figure 6
Figure 6
Arrangement format of Fcores on the Tianjic chip. (A) Reconfigurable routing tables of the routers of FCore have the ability of arbitrary connection topologies. (B) The arrangement of Fcore on the chip.
Figure 7
Figure 7
Neuromorphic synaptic apparatus spiking neural network core block.
Figure 8
Figure 8
The propagation of router data between core blocks.
Figure 9
Figure 9
Analog silicon neurons implementation.
Figure 10
Figure 10
Schematic diagram of analog neuron with cycle counting structure.
Figure 11
Figure 11
BBS chip structure.
Figure 12
Figure 12
Block diagram of the Analog Network Core (ANNCORE).
See this image and copyright information in PMC

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