Currently, AI services are spreading rapidly in everyday life and across all industries. These services are made possible by connecting AI centers and terminals such as mobile devices, PCs, etc. However, this method increases the burden on the environment by consuming a lot of power, not only to power the AI system, but also to transmit data. In times of war or disaster, it can become unusable due to power outages and network outages, the consequences of which can be even more serious if it is an AI service in terms of life and safety. As a next-generation artificial intelligence technology that can overcome these weaknesses, the low-power and high-efficiency “in-sensor computing” technology that mimics the information processing mechanism of the human nervous system is attracting attention.
The Korea Institute of Science and Technology (KIST, President Seok-Jin Yoon) announced that its team led by Dr. Suyoun Lee (Center for Neuromorphic Engineering) has succeeded in developing “artificial sensory neurons” that will be essential for the practical use of in-sensor computing. Neurons refine enormous external stimuli (received by sensory organs such as eyes, nose, mouth, ears and skin) into information in the form of spikes; and therefore play an important role in enabling the brain to quickly integrate and perform complex tasks such as cognition, learning, reasoning, prediction, and judgment with little energy.
The Ovonic Threshold Switch (OTS) is a two terminal switching device that maintains a high resistance state (10-100 MΩ) below the switching voltage and exhibits a strong decrease in resistance above the switching voltage. In a previous study, the team developed an artificial neuron device that mimics the action of neurons (integrate-and-fire) that generates a peak signal when the input signal exceeds a specific intensity.
This study, published in Nano letters, introduces a three-terminal ovonic threshold switch (3T-OTS) device that can control the switching voltage to simulate the behavior of neurons and quickly find and abstract patterns between massive amounts of data input to sensory organs. By connecting a sensor to the third electrode of the 3T-OTS device, which converts external stimuli into voltage, it was possible to realize a sensory neuron device that changes the peak patterns according to the external stimuli.
The research team managed to realize an artificial visual neuron device that mimics the information processing method of human sensory organs, by combining a 3T-OTS and a photodiode. In addition, by connecting an artificial visual neuron device to an artificial neural network that mimics the visual center of the brain, the team was able to distinguish COVID-19 infections from viral pneumonia with an accuracy of about 86.5% through image learning from chest radiographs.
dr. Suyoun Lee, director of the KIST Center for Neuromorphic Engineering, said: “This artificial sensory neuron device is a platform technology that can implement various sensory neuron devices, such as sight and touch, by connecting to existing sensors. It is a crucial building block for in sensor computer technology.”
He also explained the importance of the research that “will greatly contribute to solving various social problems related to life and safety, such as developing a diagnostic system for medical imaging that can simultaneously diagnose, predict acute heart disease through time series pattern analysis of pulse and blood pressure, and realizing extrasensory ability to detect vibrations beyond the audible frequency to prevent building collapses, earthquakes, tsunamis, etc.
Development of artificial neurofiber transistors that can be implemented in dendritic networks
Hyejin Lee et al, Three-Terminal Ovonic Threshold Switch (3T-OTS) with Adjustable Threshold Voltage for Versatile Artificial Sensory Neurons, Nano letters (2022). DOI: 10.1021/acs.nanolet.1c04125
Provided by the National Research Council of Science & Technology
Quote: Development of low-power and high-efficiency artificial sensory neurons (2022, April 8), retrieved April 8, 2022 from https://phys.org/news/2022-04-low-power-high-efficiency-artificial- sensory-neurons.html
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