A November 9, 2022 Science China Press press release on EurekAlert announces a new approach to developing neuromorphic (brainlike) devices,
Neuromorphic computing is an information processing model that simulates the efficiency of the human brain with multifunctionality and flexibility. Currently, artificial synaptic devices represented by memristors have been extensively used in neural morphological computing, and different types of neural networks have been developed. However, it is time-consuming and laborious to perform fixing and redeploying of weights stored by traditional artificial synaptic devices. Moreover, synaptic strength is primarily reconstructed via software programming and changing the pulse time, which can result in low efficiency and high energy consumption in neural morphology computing applications.
In a novel research article published in the Beijing-based National Science Review, Prof. Lili Wang from the Chinese Academy of Sciences and her colleagues present a novel hardware neural network based on a tunable flexible MXene energy storage (FMES) system. The system comprises flexible postsynaptic electrodes and MXene nanosheets, which are connected with the presynaptic electrodes using electrolytes. The potential changes in the ion migration process and adsorption in the supercapacitor can simulate information transmission in the synaptic gap. Additionally, the voltage of the FMES system represents the synaptic weight of the connection between two neurons.
Researchers explored the changes of paired-pulse facilitation under different resistance levels to investigate the effect of resistance on the advanced learning and memory behavior of the artificial synaptic system of FMES. The results revealed that the larger the standard deviation, the stronger the memory capacity of the system. In other words, with the continuous improvement of electrical resistance and stimulation time, the memory capacity of the artificial synaptic system of FMES is gradually improved. Therefore, the system can effectively control the accumulation and dissipation of ions by regulating the resistance value in the system without changing the external stimulus, which is expected to realize the coupling of sensing signals and storage weight.
The FMES system can be used to develop neural networks and realize various neural morphological computing tasks, making the recognition accuracy of handwritten digit sets reach 95%. Additionally, the FMES system can simulate the adaptivity of the human brain to achieve adaptive recognition of similar target data sets. Following the training process, the adaptive recognition accuracy can reach approximately 80%, and avoid the time and energy loss caused by recalculation.
“In the future, based on this research, different types of sensors can be integrated on the chip to further realize multimodal sensing computing integrated architecture.” Prof. Lili Wang stated, “The device can perform low-energy calculations, and is expected to solve the problems of high write noise, nonlinear difference, and diffusion under zero bias voltage in certain neural morphological systems.”
Here’s a link to and a citation for the paper,
Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices by Shufang Zhao, Wenhao Ran, Zheng Lou, Linlin Li, Swapnadeep Poddar, Lili Wang, Zhiyong Fan, Guozhen Shen. National Science Review, Volume 9, Issue 11, November 2022, nwac158, EOI: https://doi.org/10.1093/nsr/nwac158 Published: 13 August 2022
This paper is open access.
The future (or roadmap for) of Chinese research on neuromorphic engineering
While I was trying (unsuccessfully) to find a copy of the press release on the issuing agency’s website, I found this paper,
2022 roadmap on neuromorphic devices & applications research in China by Qing Wan, Changjin Wan, Huaqiang Wu, Yuchao Yang, Xiaohe Huang, Peng Zhou, LinChen, Tian-Yu Wang, Yi Li, Kanhao Xue, Yuhui He, Xiangshui Miao, Xi Li, Chenchen Xie, Houpeng Chen, Z. T. Song, Hong Wang, Yue Hao, Junyao Zhang, Jia Huang, Zheng Yu Ren, Li Qiang Zhu, Jianyu Du, Chen Ge, Yang Liu, Guanglong Ding, Ye Zhou, Su-Ting Han, Guosheng Wang, Xiao Yu, Bing Chen, Zhufei Chu, Lunyao Wang, Yinshui Xia, Chen Mu, Feng Lin, Chixiao Chen, Bojun Cheng, Yannan Xing, Weitao Zeng, Hong Chen, Lei Yu, Giacomo Indiveri and Ning Qiao. Neuromorphic Computing and Engineering DOI: 10.1088/2634-4386/ac7a5a *Accepted Manuscript online 20 June 2022 • © 2022 The Author(s). Published by IOP Publishing Ltd
The paper is open access.
*From the IOP’s Definitions of article versions: Accepted Manuscript is ‘the version of the article accepted for publication including all changes made as a result of the peer review process, and which may also include the addition to the article by IOP of a header, an article ID, a cover sheet and/or an ‘Accepted Manuscript’ watermark, but excluding any other editing, typesetting or other changes made by IOP and/or its licensors’.*
This is neither the published version nor the version of record.