Tag Archives: Jia Huang

A nontraditional artificial synaptic device and roadmap for Chinese research into neuromorphic devices

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.

Skin-like computing device analyzes health data with brain-mimicking artificial intelligence (a neuromorphic chip)

The wearable neuromorphic chip, made of stretchy semiconductors, can implement artificial intelligence (AI) to process massive amounts of health information in real time. Above, Asst. Prof. Sihong Wang shows a single neuromorphic device with three electrodes. (Photo by John Zich)

Does everything have to be ‘brainy’? Read on for the latest on ‘brainy’ devices.

An August 4, 2022 University of Chicago news release (also on EurekAlert) describes work on a stretchable neuromorphic chip, Note: Links have been removed,

It’s a brainy Band-Aid, a smart watch without the watch, and a leap forward for wearable health technologies. Researchers at the University of Chicago’s Pritzker School of Molecular Engineering (PME) have developed a flexible, stretchable computing chip that processes information by mimicking the human brain. The device, described in the journal Matter, aims to change the way health data is processed.

“With this work we’ve bridged wearable technology with artificial intelligence and machine learning to create a powerful device which can analyze health data right on our own bodies,” said Sihong Wang, a materials scientist and Assistant Professor of Molecular Engineering.

Today, getting an in-depth profile about your health requires a visit to a hospital or clinic. In the future, Wang said, people’s health could be tracked continuously by wearable electronics that can detect disease even before symptoms appear. Unobtrusive, wearable computing devices are one step toward making this vision a reality. 

A Data Deluge
The future of healthcare that Wang—and many others—envision includes wearable biosensors to track complex indicators of health including levels of oxygen, sugar, metabolites and immune molecules in people’s blood. One of the keys to making these sensors feasible is their ability to conform to the skin. As such skin-like wearable biosensors emerge and begin collecting more and more information in real-time, the analysis becomes exponentially more complex. A single piece of data must be put into the broader perspective of a patient’s history and other health parameters.

Today’s smart phones are not capable of the kind of complex analysis required to learn a patient’s baseline health measurements and pick out important signals of disease. However, cutting-edge artificial intelligence platforms that integrate machine learning to identify patterns in extremely complex datasets can do a better job. But sending information from a device to a centralized AI location is not ideal.

“Sending health data wirelessly is slow and presents a number of privacy concerns,” he said. “It is also incredibly energy inefficient; the more data we start collecting, the more energy these transmissions will start using.”

Skin and Brains
Wang’s team set out to design a chip that could collect data from multiple biosensors and draw conclusions about a person’s health using cutting-edge machine learning approaches. Importantly, they wanted it to be wearable on the body and integrate seamlessly with skin.

“With a smart watch, there’s always a gap,” said Wang. “We wanted something that can achieve very intimate contact and accommodate the movement of skin.”

Wang and his colleagues turned to polymers, which can be used to build semiconductors and electrochemical transistors but also have the ability to stretch and bend. They assembled polymers into a device that allowed the artificial-intelligence-based analysis of health data. Rather than work like a typical computer, the chip— called a neuromorphic computing chip—functions more like a human brain, able to both store and analyze data in an integrated way.

Testing the Technology
To test the utility of their new device, Wang’s group used it to analyze electrocardiogram (ECG) data representing the electrical activity of the human heart. They trained the device to classify ECGs into five categories—healthy or four types of abnormal signals. Then, they tested it on new ECGs. Whether or not the chip was stretched or bent, they showed, it could accurately classify the heartbeats.

More work is needed to test the power of the device in deducing patterns of health and disease. But eventually, it could be used either to send patients or clinicians alerts, or to automatically tweak medications.

“If you can get real-time information on blood pressure, for instance, this device could very intelligently make decisions about when to adjust the patient’s blood pressure medication levels,” said Wang. That kind of automatic feedback loop is already used by some implantable insulin pumps, he added.

He already is planning new iterations of the device to both expand the type of devices with which it can integrate and the types of machine learning algorithms it uses.

“Integration of artificial intelligence with wearable electronics is becoming a very active landscape,” said Wang. “This is not finished research, it’s just a starting point.”

Here’s a link to and a citation for the paper,

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence by Shilei Dai, Yahao Dai, Zixuan Zhao, Jie Xu, Jia Huang, Sihong Wang. Matter DOI:https://doi.org/10.1016/j.matt.2022.07.016 Published: August 04, 2022

This paper is behind a paywall.