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PICASSO Technique Drives Biological Molecules into Technicolor
The new imaging approach brings current imaging colors from four to more than 15 for mapping overlapping proteins Pablo Picasso’s surreal cubist artistic style shifted common features into unrecognizable scenes, but a new imaging approach bearing his namesake may elucidate the most complicated subject: the brain. Employing artificial intelligence to clarify spectral color blending of tiny molecules used to stain specific proteins and other items of research interest, the PICASSO technique, allows researchers to use more than 15 colors to image and parse our overlapping proteins. The PICASSO developers, based in Korea, published their approach on May 5 in Nature Communications. Fluorophores — the staining molecules — emit specific colors when excited by a light, but if more than four fluorophores are used, their emitted colors overlap and blend. Researchers previously developed techniques to correct this spectral overlap by precisely defining the matrix of mixed and unmixed images. This measurement depends on reference spectra, found by identifying clear images of only one fluorophore-stained specimen or of multiple, identically prepared specimens that only contain a single fluorophore each. “Such reference spectra measurement could be complicated to perform in highly heterogeneous specimens, such as the brain, due to the highly varied emission spectra of fluorophores depending on the subregions from which the spectra were measured,” said co-corresponding author Young-Gyu Yoon, professor in the School of Electrical Engineering at KAIST. He explained that the subregions would each need their own spectra reference measurements, making for an inefficient, time-consuming process. “To address this problem, we developed an approach that does not require reference spectra measurements.” The approach is the “Process of ultra-multiplexed Imaging of biomolecules viA the unmixing of the Signals of Spectrally Overlapping fluorophores,” also known as PICASSO. Ultra-multiplexed imaging refers to visualizing the numerous individual components of a unit. Like a cinema multiplex in which each theater plays a different movie, each protein in a cell has a different role. By staining with fluorophores, researchers can begin to understand those roles. “We devised a strategy based on information theory; unmixing is performed by iteratively minimizing the mutual information between mixed images,” said co-corresponding author Jae-Byum Chang, professor in the Department of Materials Science and Engineering, KAIST. “This allows us to get away with the assumption that the spatial distribution of different proteins is mutually exclusive and enables accurate information unmixing.” To demonstrate PICASSO’s capabilities, the researchers applied the technique to imaging a mouse brain. With a single round of staining, they performed 15-color multiplexed imaging of a mouse brain. Although small, mouse brains are still complex, multifaceted organs that can take significant resources to map. According to the researchers, PICASSO can improve the capabilities of other imaging techniques and allow for the use of even more fluorophore colors. Using one such imaging technique in combination with PICASSO, the team achieved 45-color multiplexed imaging of the mouse brain in only three staining and imaging cycles, according to Yoon. “PICASSO is a versatile tool for the multiplexed biomolecule imaging of cultured cells, tissue slices and clinical specimens,” Chang said. “We anticipate that PICASSO will be useful for a broad range of applications for which biomolecules’ spatial information is important. One such application the tool would be useful for is revealing the cellular heterogeneities of tumor microenvironments, especially the heterogeneous populations of immune cells, which are closely related to cancer prognoses and the efficacy of cancer therapies.” The Samsung Research Funding & Incubation Center for Future Technology supported this work. Spectral imaging was performed at the Korea Basic Science Institute Western Seoul Center. -PublicationJunyoung Seo, Yeonbo Sim, Jeewon Kim, Hyunwoo Kim, In Cho, Hoyeon Nam, Yong-Gyu Yoon, Jae-Byum Chang, “PICASSO allows ultra-multiplexed fluorescence imaging of spatiallyoverlapping proteins without reference spectra measurements,” May 5, Nature Communications (doi.org/10.1038/s41467-022-30168-z) -ProfileProfessor Jae-Byum ChangDepartment of Materials Science and EngineeringCollege of EngineeringKAIST Professor Young-Gyu YoonSchool of Electrical EngineeringCollege of EngineeringKAIST
KAIST & LG U+ Team Up for Quantum Computing Solution for Ultra-Space 6G Satellite Networking
KAIST quantum computer scientists have optimized ultra-space 6G Low-Earth Orbit (LEO) satellite networking, finding the shortest path to transfer data from a city to another place via multi-satellite hops. The research team led by Professor June-Koo Kevin Rhee and Professor Dongsu Han in partnership with LG U+ verified the possibility of ultra-performance and precision communication with satellite networks using D-Wave, the first commercialized quantum computer. Satellite network optimization has remained challenging since the network needs to be reconfigured whenever satellites approach other satellites within the connection range in a three-dimensional space. Moreover, LEO satellites orbiting at 200~2000 km above the Earth change their positions dynamically, whereas Geo-Stationary Orbit (GSO) satellites do not change their positions. Thus, LEO satellite network optimization needs to be solved in real time. The research groups formulated the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and managed to solve the problem, incorporating the connectivity and link distance limits as the constraints. The proposed optimization algorithm is reported to be much more efficient in terms of hop counts and path length than previously reported studies using classical solutions. These results verify that a satellite network can provide ultra-performance (over 1Gbps user-perceived speed), and ultra-precision (less than 5ms end-to-end latency) network services, which are comparable to terrestrial communication. Once QUBO is applied, “ultra-space networking” is expected to be realized with 6G. Researchers said that an ultra-space network provides communication services for an object moving at up to 10 km altitude with an extreme speed (~ 1000 km/h). Optimized LEO satellite networks can provide 6G communication services to currently unavailable areas such as air flights and deserts. Professor Rhee, who is also the CEO of Qunova Computing, noted, “Collaboration with LG U+ was meaningful as we were able to find an industrial application for a quantum computer. We look forward to more quantum application research on real problems such as in communications, drug and material discovery, logistics, and fintech industries.”
New Polymer Mesophase Structure Discovered
Bilayer-folded lamellar mesophase induced by random polymer sequence Polymers, large molecules made up of repeating smaller molecules called monomers, are found in nearly everything we use in our day-to-day lives. Polymers can be natural or created synthetically. Natural polymers, also called biopolymers, include DNA, proteins, and materials like silk, gelatin, and collagen. Synthetic polymers make up many different kinds of materials, including plastic, that are used in constructing everything from toys to industrial fiber cables to brake pads. As polymers are formed through a process called polymerization, the monomers are connected through a chain. As the chain develops, the structure of the polymer determines its unique physical and chemical properties. Researchers are continually studying polymers, how they form, how they are structured, and how they develop these unique properties. By understanding this information, scientists can develop new uses for polymers and create new materials that can be used in a wide variety of industries. In a paper published in Nature Communications on May 4, researchers describe a new structure found in an aqueous solution of an amphiphilic copolymer, called a bilayer-folded lamellar mesophase, that has been discovered through a random copolymer sequence. “A new mesophase is an important discovery as it shows a new way for molecules to self-organize,” said Professor Myungeun Seo at the Department of Chemistry at KAIST. “We were particularly thrilled to identify this bilayer-folded lamellar phase because pure bilayer membranes are difficult to fold thermodynamically.” Researchers think that this mesophase structure comes from the sequence of the monomers within the copolymer. The way the different monomers arrange themselves in the chain that makes up a copolymer is important and can have implications for what the copolymer can do. Many copolymers are random, which means that their structure relies on how the monomers interact with each other. In this case, the interaction between the hydrophobic monomers associates the copolymer chains to conceal the hydrophobic domain from water. As the structure gets more complex, researchers have found that a visible order develops so that monomers can be matched up with the right pair. “While we tend to think random means disorder, here we showed that a periodic order can spontaneously arise from the random copolymer sequence based on their collective behavior,” said Professor Seo. “We believe this comes from the sequence matching problem: finding a perfectly complementary pair for a long sequence is nearly impossible.” This is what creates the unique structure of this newly discovered mesophase. The copolymer spontaneously folds and creates a multilamellar structure that is separated by water. A multilamellar structure refers to plate-like folds and the folded layers stack on top of each other. The resulting mesophase is birefringent, meaning light refracts through it, it is similar to liquid crystalline, and viscoelastic, which means that it is both viscous and elastic at the same time. Looking ahead, researchers hope to learn more about this new mesophase and figure out how to control the outcome. Once more is understood about the mesophase and how it is formed, it’s possible that new mesophases could be discovered as more sequences are researched. “One of the obvious questions for us is how to control the folding frequency and adjust the folded height, which we are currently working to address. Ultimately, we want to understand how different multinary sequences can associate with another to create order and apply the knowledge to develop new materials,” said Professor Seo. The National Research Foundation, the Ministry of Education, and the Ministry of Science and ICT of Korea funded this research. -PublicationMinjoong Shin, Hayeon Kim, Geonhyeong Park, Jongmin Park, Hyungju Ahn, Dong Ki Yoon, Eunji Lee, Myungeun Seo, “Bilayer-folded lamellar mesophase induced by random polymersequence,” May 4, 2022, Nature Communications (https://doi.org/10.1038/s41467-022-30122-z) -ProfileProfessor Myungeun SeoMacromolecular Materials Chemistry Lab (https://nanopsg.kaist.ac.kr/)Department of ChemistryCollege of Natural SciencesKAIST
Professor Juho Kim’s Team Wins Best Paper Award at ACM CHI 2022
The research team led by Professor Juho Kim from the KAIST School of Computing won a Best Paper Award and an Honorable Mention Award at the Association for Computing Machinery Conference on Human Factors in Computing Systems (ACM CHI) held between April 30 and May 6. ACM CHI is the world’s most recognized conference in the field of human computer interactions (HCI), and is ranked number one out of all HCI-related journals and conferences based on Google Scholar’s h-5 index. Best paper awards are given to works that rank in the top one percent, and honorable mention awards are given to the top five percent of the papers accepted by the conference. Professor Juho Kim presented a total of seven papers at ACM CHI 2022, and tied for the largest number of papers. A total of 19 papers were affiliated with KAIST, putting it fifth out of all participating institutes and thereby proving KAIST’s competence in research. One of Professor Kim’s research teams composed of Jeongyeon Kim (first author, MS graduate) from the School of Computing, MS candidate Yubin Choi from the School of Electrical Engineering, and Dr. Meng Xia (post-doctoral associate in the School of Computing, currently a post-doctoral associate at Carnegie Mellon University) received a best paper award for their paper, “Mobile-Friendly Content Design for MOOCs: Challenges, Requirements, and Design Opportunities”. The study analyzed the difficulties experienced by learners watching video-based educational content in a mobile environment and suggests guidelines for solutions. The research team analyzed 134 survey responses and 21 interviews, and revealed that texts that are too small or overcrowded are what mainly brings down the legibility of video contents. Additionally, lighting, noise, and surrounding environments that change frequently are also important factors that may disturb a learning experience. Based on these findings, the team analyzed the aptness of 41,722 frames from 101 video lectures for mobile environments, and confirmed that they generally show low levels of adequacy. For instance, in the case of text sizes, only 24.5% of the frames were shown to be adequate for learning in mobile environments. To overcome this issue, the research team suggested a guideline that may improve the legibility of video contents and help overcome the difficulties arising from mobile learning environments. The importance of and dependency on video-based learning continue to rise, especially in the wake of the pandemic, and it is meaningful that this research suggested a means to analyze and tackle the difficulties of users that learn from the small screens of mobile devices. Furthermore, the paper also suggested technology that can solve problems related to video-based learning through human-AI collaborations, enhancing existing video lectures and improving learning experiences. This technology can be applied to various video-based platforms and content creation. Meanwhile, a research team composed of Ph.D. candidate Tae Soo Kim (first author), MS candidate DaEun Choi, and Ph.D. candidate Yoonseo Choi from the School of Computing received an honorable mention award for their paper, “Stylette: styling the Web with Natural Language”. The research team developed a novel interface technology that allows nonexperts who are unfamiliar with technical jargon to edit website features through speech. People often find it difficult to use or find the information they need from various websites due to accessibility issues, device-related constraints, inconvenient design, style preferences, etc. However, it is not easy for laymen to edit website features without expertise in programming or design, and most end up just putting up with the inconveniences. But what if the system could read the intentions of its users from their everyday language like “emphasize this part a little more”, or “I want a more modern design”, and edit the features automatically? Based on this question, Professor Kim’s research team developed ‘Stylette’, a system in which AI analyses its users’ speech expressed in their natural language and automatically recommends a new style that best fits their intentions. The research team created a new system by putting together language AI, visual AI, and user interface technologies. On the linguistic side, a large-scale language model AI converts the intentions of the users expressed through their everyday language into adequate style elements. On the visual side, computer vision AI compares 1.7 million existing web design features and recommends a style adequate for the current website. In an experiment where 40 nonexperts were asked to edit a website design, the subjects that used this system showed double the success rate in a time span that was 35% shorter compared to the control group. It is meaningful that this research proposed a practical case in which AI technology constructs intuitive interactions with users. The developed technology can be applied to existing design applications and web browsers in a plug-in format, and can be utilized to improve websites or for advertisements by collecting the natural intention data of users on a large scale.
Professor Lickho Song Publishes a Book on Probability and Random Variables in English
Professor Lickho Song from the School of Electrical Engineering has published a book on probability and random variables in English. This is the translated version of his book in Korean ‘Theory of Random Variables’, which was selected as an Excellent Book of Basic Sciences by the National Academy of Sciences and the Ministry of Education in 2020. The book discusses diverse concepts, notions, and applications concerning probability and random variables, explaining basic concepts and results in a clearer and more complete manner. Readers will also find unique results on the explicit general formula of joint moments and the expected values of nonlinear functions for normal random vectors. In addition, interesting applications for the step and impulse functions in discussions on random vectors are presented. Thanks to a wealth of examples and a total of 330 practice problems of varying difficulty, readers will have the opportunity to significantly expand their knowledge and skills. The book includes an extensive index, allowing readers to quickly and easily find what they are looking for. It also offers a valuable reference guide for experienced scholars and professionals, helping them review and refine their expertise. Link: https://link.springer.com/book/10.1007/978-3-030-97679-8
Professor Sang Kil Cha Receives IEEE Test-of-Time Award
Professor Sang Kil Cha from the Graduate School of Information Security (GSIS) in the School of Computing received the Test-of-Time Award from IEEE Security & Privacy, a top conference in the field of information security. The Test-of-Time Award recognizes the research papers that have influenced the field of information security the most over the past decade. Three papers were selected this year, and Professor Cha is the first Korean winner of the award. The paper by Professor Cha was published in 2012 under the title, “Unleashing Mayhem on Binary Code”. It was the first to ever suggest an algorithm that automatically finds bugs in binary code and creates exploits that links them to an attack code. The developed algorithm is a core technique used for world-class cyber security hacking competitions like the Cyber Grand Challenge, an AI hacking contest. Starting with this research, Professor Cha has carried out various studies to develop technologies that can find bugs and vulnerabilities through binary analyses, and is currently developing B2R2, a Korean platform that can analyze various binary codes.
Professor Jae-Woong Jeong Receives Hyonwoo KAIST Academic Award
Professor Jae-Woong Jeong from the School of Electrical Engineering was selected for the Hyonwoo KAIST Academic Award, funded by the HyonWoo Cultural Foundation (Chairman Soo-il Kwak, honorary professor at Seoul National University Business School). The Hyonwoo KAIST Academic Award, presented for the first time in 2021, is an award newly founded by the donations of Chairman Soo-il Kwak of the HyonWoo Cultural Foundation, who aims to reward excellent KAIST scholars who have made outstanding academic achievements. Every year, through the strict evaluations of the selection committee of the HyonWoo Cultural Foundation and the faculty reward recommendation board, KAIST will choose one faculty member that may represent the school with their excellent academic achievement, and reward them with a plaque and 100 million won. Professor Jae-Woong Jeong, the winner of this year’s award, developed the first IoT-based wireless remote brain neural network control system to overcome brain diseases, and has been leading the field. The research was published in 2021 in Nature Biomedical Engineering, one of world’s best scientific journals, and has been recognized as a novel technology that suggested a new vision for the automation of brain research and disease treatment. This study, led by Professor Jeong’s research team, was part of the KAIST College of Engineering Global Initiative Interdisciplinary Research Project, and was jointly studied by Washington University School of Medicine through an international research collaboration. The technology was introduced more than 60 times through both domestic and international media, including Medical Xpress, MBC News, and Maeil Business News. Professor Jeong has also developed a wirelessly chargeable soft machine for brain transplants, and the results were published in Nature Communications. He thereby opened a new paradigm for implantable semi-permanent devices for transplants, and is making unprecedented research achievements.
2022 KAIST Research Day Recognizes 10 Outstanding Researches
On May 31, the 2022 KAIST Research Day was held at the Jeongo Geun-mo Conference Hall at KAIST’s main campus. Since 2016, Research Day has been a yearly festival for researchers at KAIST. By introducing major research achievements and providing opportunities for information exchanges in R&D, it aims to create an atmosphere for mutual cooperation and communication amongst researchers, thereby vitalizing interdisciplinary research. At this year’s event, 10 faculty members and their representative research achievements were rewarded. As the winner of the Grand Prize for Research, Professor Il-Doo Kim (Department of Materials Science and Engineering) gave a lecture on his topic, “Ultrasensitive flexible chemical sensor”. With rising attention being given to environmental safety and healthcare, the importance of mobile sensors for trace amounts of molecules that can quickly raise hazard signals and allow early diagnosis from breath analysis have been brought to light. The lecture will break down ultrasensitive chemical sensor development cases, and introduced how gas sensor technologies developed at KAIST in particular are being applied at semiconductor and display fabrication plants for environmental and safety analyses and hazard prevention. Professor Il-Doo Kim is a recognized researcher for his inventive achievements in the fields of respiratory gas sensor technology for early disease monitoring, and ordered nanofiber membranes for antiviral and fine dust filters. Professor Kim has so far published 343 international research papers, received 56 journal covers, been awarded 230 domestic and international patents, and completed 12 technology transfers. He has also received a presidential award on the 51st invention day in 2016, Scientist of the Year Award selected by reporters in 2019, and has been named a fellow in the engineering division of the Korean Academy of Science and Technology in 2022. Professor Kwang-Hyun Cho at the Department of Bio and Brain Engineering and Professor Doh Chang Lee at the Department of Chemical and Biomolecular Engineering were each awarded the Research Award, and Professor Dongsoo Han at the School of Computing received the Innovation Award. Professors Buhm Soon Park at the Graduate School of Science and Technology Policy, Changick Kim at the School of Electrical Engineering and Hyun Jung Cho at the School of Digital Humanities and Computational Social Sciences received the Interdisciplinary Research Award as a team. The passion and experiences of the awardees are to be introduced to undergraduate and graduate students as well as fellow researchers through a pre-recorded video lecture, while the lecture of the winner of the grand prize will be delivered on site. Meanwhile, the top ten R&D achievements of KAIST selected excellent research outcomes from the natural and biological sciences including “Polariton-based PT symmetry laser that turns loss into gain” (Professor Yong-Hoon Cho at the Department of Physics), “Solution to the Riemann Problem including weak shock waves in 1-dimensional space” (Professor Moon-Jin Kang at the Department of Mathematical Sciences), and “Characterization of immune reaction in COVID-19 patients” (Professor Eui-Cheol Shin at the Graduate School of Medical Science and Engineering.) Awardees from the engineering field included “Fluid surface stabilization technology using plasma jet” (Professor Wonho Choe at the Department of Nuclear and Quantum Engineering, “Visual recognition technology using event-based cameras” (Professor Kuk-Jin Yoon at theDepartment of Mechanical Engineering, “Artificial sensory system development through neural signal mimicry” (Professor Seongjun Park at the Department of Bio and Brain Engineering, “Mott transition material-based ultrahigh speed, low-power, and deformation-resistant true random number generator” (Professor Kyung Min Kim at the Department of Materials Science and Engineering, “Investment service design based on Aline: ESG” (Professor Sangsu Lee at the Department of Industrial Design), “Structural color printing technology without chemical colorings” (Professor Shin-Hyun Kim at the Department of Chemical and Biomolecular Engineering), and “Differentiable transient optical transfer simulation” (Professor Minhyuk Kim at the School of Computing) To encourage the participation of members of KAIST, all parts of the ceremony will be broadcast live through YouTube in both English and Korean.” He added, “Offline audiences will congratulate the awardees at Fusion Hall in the KI Building and gain research ideas.”
Game Design Guide Book for Middle-Aged and Older Adult Players Helps Rewrite Gaming Culture
The online book ‘Game Design Guide for Adults in Their 50s and Older’ helps to increase accessibility for adult gamers A KAIST multi-disciplinary research team published a game guide to respond to the new demands of senior gamers and expand the gaming market. The guide will be helpful for designing interfaces fit for senior groups as a way to minimize the cognitive burdens related to aging. It also helps readers understand older users’ cognitive abilities and socioemotional characteristics. “This guide analyzed the game experience of players in their 50s and older and converted it into a game design element that can be easily referred to by game developers and designers,” explained Professor Young Im Do from the Graduate School of Culture Technology who led the research. The gaming industry is paying attention to the emerging trend of ‘active aging’ and senior gamers. According to the National Purchase Diary Panel Inc., game play time increased significantly in the 45-64 age group compared to other age groups during the pandemic. Despite the growing number of senior gamers, it is still difficult for older novice players to start video games because most commercial games focus on younger players. For example, older players can feel frustrated if the game requires fast reflexes and accurate timing. Font sizes and objects that are too small as well as interfaces that are too complicated can be challenging for senior gamers. The research team presents how to handle these difficulties in game design considering the visual-motor coordination of people in age groups ranging from their 20s to 80s. It also proposes various game elements such as audio-visual elements, cognitive and motor elements, game rules, stories and characters, social aspects, in-app purchases, and advertisements for senior groups. The guide also proposes a game service model and introduces examples of game prototypes that apply supportive technology. For this guide, the researchers operated the “International Game Living Lab”, which is an open space for creating novel and innovative solutions by converging IT technology into daily life. In the lab, ordinary citizens, research institutes, companies, and local communities formed a cooperative network and actively participated in experiments, education, and discussions for finding solutions over three years. Researchers in multi-disciplinary fields, including computer science, psychology, game design, and gerontechnology, covered various methodologies to understand the game experience of adults in their 50s and older. In order to profile players of this age group, three different approaches were performed: visual-motor coordination experiments, an EEG (Electroencephalogram) test, and a gameplay workshop. Then, they converted the results into practical knowledge that can be used in the gaming industry. Professor Kyung Myun Lee from the School of Digital Humanities and Computational Social Sciences at KAIST, Professor Byungjoo Shin from Yonsei University, CEO Junyoung Shin of CareU, and CEO Minseok Doh of Heartverse participated in this online book which is available to the public at https://wikidocs.net/book/7356.
Now You Can See Floral Scents!
Optical interferometry visualizes how often lilies emit volatile organic compounds Have you ever thought about when flowers emit their scents? KAIST mechanical engineers and biological scientists directly visualized how often a lily releases a floral scent using a laser interferometry method. These measurement results can provide new insights for understanding and further exploring the biosynthesis and emission mechanisms of floral volatiles. Why is it important to know this? It is well known that the fragrance of flowers affects their interactions with pollinators, microorganisms, and florivores. For instance, many flowering plants can tune their scent emission rates when pollinators are active for pollination. Petunias and the wild tobacco Nicotiana attenuata emit floral scents at night to attract night-active pollinators. Thus, visualizing scent emissions can help us understand the ecological evolution of plant-pollinator interactions. Many groups have been trying to develop methods for scent analysis. Mass spectrometry has been one widely used method for investigating the fragrance of flowers. Although mass spectrometry reveals the quality and quantity of floral scents, it is impossible to directly measure the releasing frequency. A laser-based gas detection system and a smartphone-based detection system using chemo-responsive dyes have also been used to measure volatile organic compounds (VOCs) in real-time, but it is still hard to measure the time-dependent emission rate of floral scents. However, the KAIST research team co-led by Professor Hyoungsoo Kim from the Department of Mechanical Engineering and Professor Sang-Gyu Kim from the Department of Biological Sciences measured a refractive index difference between the vapor of the VOCs of lilies and the air to measure the emission frequency. The floral scent vapor was detected and the refractive index of air was 1.0 while that of the major floral scent of a linalool lily was 1.46. Professor Hyoungsoo Kim said, “We expect this technology to be further applicable to various industrial sectors such as developing it to detect hazardous substances in a space.” The research team also plans to identify the DNA mechanism that controls floral scent secretion. The current work entitled “Real-time visualization of scent accumulation reveals the frequency of floral scent emissions” was published in ‘Frontiers in Plant Science’ on April 18, 2022. (https://doi.org/10.3389/fpls.2022.835305). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2021R1A2C2007835), the Rural Development Administration (PJ016403), and the KAIST-funded Global Singularity Research PREP-Program. -Publication:H. Kim, G. Lee, J. Song, and S.-G. Kim, "Real-time visualization of scent accumulation reveals the frequency of floral scent emissions," Frontiers in Plant Science 18, 835305 (2022) (https://doi.org/10.3389/fpls.2022.835305) -Profile:Professor Hyoungsoo Kimhttp://fil.kaist.ac.kr @MadeInH on TwitterDepartment of Mechanical EngineeringKAIST Professor Sang-Gyu Kimhttps://sites.google.com/view/kimlab/home Department of Biological SciencesKAIST
Neuromorphic Memory Device Simulates Neurons and Synapses
Simultaneous emulation of neuronal and synaptic properties promotes the development of brain-like artificial intelligence Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices. Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices. Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency. The human brain consists of a complex network of 100 billion neurons and 100 trillion synapses. The functions and structures of neurons and synapses can flexibly change according to the external stimuli, adapting to the surrounding environment. The research team developed a neuromorphic device in which short-term and long-term memories coexist using volatile and non-volatile memory devices that mimic the characteristics of neurons and synapses, respectively. A threshold switch device is used as volatile memory and phase-change memory is used as a non-volatile device. Two thin-film devices are integrated without intermediate electrodes, implementing the functional adaptability of neurons and synapses in the neuromorphic memory. Professor Keon Jae Lee explained, "Neurons and synapses interact with each other to establish cognitive functions such as memory and learning, so simulating both is an essential element for brain-inspired artificial intelligence. The developed neuromorphic memory device also mimics the retraining effect that allows quick learning of the forgotten information by implementing a positive feedback effect between neurons and synapses.” This result entitled “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse” was published in the May 19, 2022 issue of Nature Communications. -Publication:Sang Hyun Sung, Tae Jin Kim, Hyera Shin, Tae Hong Im, and Keon Jae Lee (2022) “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse,” Nature Communications May 19, 2022 (DOI: 10.1038/s41467-022-30432-2) -Profile:Professor Keon Jae Leehttp://fand.kaist.ac.kr Department of Materials Science and EngineeringKAIST
Energy-Efficient AI Hardware Technology Via a Brain-Inspired Stashing System
Researchers demonstrate neuromodulation-inspired stashing system for the energy-efficient learning of a spiking neural network using a self-rectifying memristor array Researchers have proposed a novel system inspired by the neuromodulation of the brain, referred to as a ‘stashing system,’ that requires less energy consumption. The research group led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a technology that can efficiently handle mathematical operations for artificial intelligence by imitating the continuous changes in the topology of the neural network according to the situation. The human brain changes its neural topology in real time, learning to store or recall memories as needed. The research group presented a new artificial intelligence learning method that directly implements these neural coordination circuit configurations. Research on artificial intelligence is becoming very active, and the development of artificial intelligence-based electronic devices and product releases are accelerating, especially in the Fourth Industrial Revolution age. To implement artificial intelligence in electronic devices, customized hardware development should also be supported. However most electronic devices for artificial intelligence require high power consumption and highly integrated memory arrays for large-scale tasks. It has been challenging to solve these power consumption and integration limitations, and efforts have been made to find out how the human brain solves problems. To prove the efficiency of the developed technology, the research group created artificial neural network hardware equipped with a self-rectifying synaptic array and algorithm called a ‘stashing system’ that was developed to conduct artificial intelligence learning. As a result, it was able to reduce energy by 37% within the stashing system without any accuracy degradation. This result proves that emulating the neuromodulation in humans is possible. Professor Kim said, "In this study, we implemented the learning method of the human brain with only a simple circuit composition and through this we were able to reduce the energy needed by nearly 40 percent.” This neuromodulation-inspired stashing system that mimics the brain’s neural activity is compatible with existing electronic devices and commercialized semiconductor hardware. It is expected to be used in the design of next-generation semiconductor chips for artificial intelligence. This study was published in Advanced Functional Materials in March 2022 and supported by KAIST, the National Research Foundation of Korea, the National NanoFab Center, and SK Hynix. -Publication: Woon Hyung Cheong, Jae Bum Jeon†, Jae Hyun In, Geunyoung Kim, Hanchan Song, Janho An, Juseong Park, Young Seok Kim, Cheol Seong Hwang, and Kyung Min Kim (2022) “Demonstration of Neuromodulation-inspired Stashing System for Energy-efficient Learning of Spiking Neural Network using a Self-Rectifying Memristor Array,” Advanced FunctionalMaterials March 31, 2022 (DOI: 10.1002/adfm.202200337) -Profile: Professor Kyung Min Kimhttp://semi.kaist.ac.kr https://scholar.google.com/citations?user=BGw8yDYAAAAJ&hl=ko Department of Materials Science and EngineeringKAIST
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