Abstract
We introduce a neuromorphic architecture devised by mapping biologically inspired concepts onto a computational framework for self-awareness, offering a new way to embed inherent autonomy directly within hardware. Leveraging this architecture as a blueprint, we have designed a memristor-based circuit capable of autonomous decision-making, employing a bio-inspired hierarchical design approach. The core of this approach involves translating the functions of biological neurons and synapses into memristor-based circuits, which act as efficient computational units at the bottom layer of the circuit. It ensures that the developed circuit strikes a balance between functional flexibility and computational efficiency. Experimental results show that our implemented circuit provides efficient computational capabilities for effective decision-making, thanks to its memristor-based bio-inspired hierarchical implementation. Furthermore, its neuromorphic architecture inspired by self-awareness facilitates the easy simulation of reliable autonomous behaviors. Through the analysis of hardware overhead and power consumption, our circuit proves to be hardware-friendly. Our work represents progress towards developing memristor-based neuromorphic circuits characterized by high computational performance and autonomous functionality.
Original language | English |
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Title of host publication | 2024 International Joint Conference on Neural Networks, IJCNN 2024 Conference Proceedings |
Publisher | IEEE |
Pages | 1-10 |
Number of pages | 10 |
ISBN (Electronic) | 9798350359312 |
ISBN (Print) | 9798350359329 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 International Joint Conference on Neural Networks (IJCNN) - Yokohama, Japan, Yokohama, Japan Duration: 30 Jun 2024 → 5 Jul 2024 |
Publication series
Name | Proceedings of the International Joint Conference on Neural Networks |
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Publisher | IEEE |
ISSN (Print) | 2161-4393 |
ISSN (Electronic) | 2161-4407 |
Conference
Conference | 2024 International Joint Conference on Neural Networks (IJCNN) |
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Country/Territory | Japan |
City | Yokohama |
Period | 30/06/24 → 5/07/24 |
Funding
This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 62206121), the Shenzhen Higher Education Stable Support Program General Project (Grant No. GXWD20231130200138001), and the Program for Guangdong Provincial Key Laboratory (Grant No. 2020B121201001).
Keywords
- Self-awareness
- autonomous decision-making
- circuit implementation
- memristor
- neuromorphic architecture