Journal
MATERIALS HORIZONS
Volume 8, Issue 2, Pages 538-546Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d0mh01730b
Keywords
-
Funding
- NSFC [92064009, 61522404]
- Shanghai Rising-Star Program [19QA1400600]
- Program of Shanghai Subject Chief Scientist [18XD1402800]
- Zhejiang Lab's International Talent Fund for Young Professionals
- Support Plans for the Youth Top-Notch Talents of China
Ask authors/readers for more resources
This paper proposes a flexible low-dimensional memristor based on boron nitride, which features ultra-low power consumption, reliable digital memcomputing capabilities, and ultrafast neuromorphic computing capabilities. The memristor successfully implements logic-in-memory operations like FALSE, IMP, and NAND, opening up new possibilities for the development of next-generation in-memory computing architectures.
The data processing efficiency of traditional computers is suffering from the intrinsic limitation of physically separated processing and memory units. Logic-in-memory and brain-inspired neuromorphic computing are promising in-memory computing paradigms for improving the computing efficiency and avoiding high power consumption caused by extra data movement. However, memristors that can conduct digital memcomputing and neuromorphic computing simultaneously are limited by the difference in the information form between digital data and analogue data. In order to solve this problem, this paper proposes a flexible low-dimensional memristor based on boron nitride (BN), which has ultralow-power non-volatile memory characteristic, reliable digital memcomputing capabilities, and integrated ultrafast neuromorphic computing capabilities in a single in situ computing system. The logic-inmemory basis, including FALSE, material implication (IMP), and NAND, are implemented successfully. The power consumption of the proposed memristor per synaptic event (198 fJ) can be as low as biology (fJ level) and the response time (1 mu s) of the neuromorphic computing is four orders of magnitude shorter than that of the human brain (10 ms), paving the way for wearable ultrahigh efficient next-generation in-memory computing architectures.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available