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A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Yuzheng Guo Orcid Logo, Xiaolong Zou, Yifan Hu, Yifei Yang, Xinxin Wang, Yuhan He, Ruikai Kong, Yuzheng Guo, Guoqi Li, Wei Zhang, Si Wu, Huanglong Li

Advanced Intelligent Systems, Volume: 3, Issue: 11, Pages: 2100054 - 2100054

Swansea University Author: Yuzheng Guo Orcid Logo

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DOI (Published version): 10.1002/aisy.202100054

Abstract

Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic...

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Published in: Advanced Intelligent Systems
ISSN: 2640-4567 2640-4567
Published: Wiley 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58919
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Abstract: Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three-level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low-level knowledge (implementations and algorithms) to achieve high-level brain-like (human-level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab-on-neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.
Keywords: algorithm levels; codesigns; computational levels; David Marr; implementation levels; neuromorphics; three-level analytical frameworks
College: Faculty of Science and Engineering
Funders: National Natural Science Foundation of China. Grant Numbers: 61974082, 61704096, 61836004; Youth Elite Scientist Sponsorship (YESS) Program of China Association for Science and Technology (CAST). Grant Number: 2019QNRC001; Supercomputing Wales. Grant Number: scw1070; National Key R&D Program of China. Grant Number: 2018YFE0200200; Tsinghua-IDG/McGovern Brain-X Program, Beijing Science and Technology Program. Grant Numbers: Z181100001518006, Z191100007519009, 2016SZ0102; CETC Haikang Group-Brain Inspired Computing Joint Research Center
Issue: 11
Start Page: 2100054
End Page: 2100054