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A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems
,
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
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© 2021 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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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...
| Published in: | Advanced Intelligent Systems |
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| ISSN: | 2640-4567 2640-4567 |
| Published: |
Wiley
2021
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| Online Access: |
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| 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. |
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| 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 |