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EE Prof. Jung-Yong Lee research team developed a high-efficiency and high-stability organic-inorganic hybrid solar cell production technology that maximizes near-infrared light capture

<Photo. (From left) Professor Jung-Yong Lee, Ph.D. candidate Min-Ho Lee, and Master’s candidate Min Seok Kim of the School of Electrical Engineering>

Existing perovskite solar cells, which have the problem of not being able to utilize approximately 52% of total solar energy, have been developed by a Korean research team as an innovative technology that maximizes near-infrared light capture performance while greatly improving power conversion efficiency. This greatly increases the possibility of commercializing next-generation solar cells and is expected to contribute to important technological advancements in the global solar cell market.

The research team of Professor Jung-Yong Lee of the KAIST EE and Professor Woojae Kim of the Department of Chemistry at Yonsei University developed a high-efficiency and high-stability organic-inorganic hybrid solar cell production technology that maximizes near-infrared light capture beyond the existing visible light range.

The research team suggested and advanced a hybrid next-generation device structure with organic photo-semiconductors that complements perovskite materials limited to visible light absorption and expands the absorption range to near-infrared.

In addition, they revealed the electronic structure problem that mainly occurs in the structure and announced a high-performance solar cell device that dramatically solved this problem by introducing a dipole layer*. *Dipole layer: A thin material layer that controls the energy level within the device to facilitate charge transport and forms an interface potential difference to improve device performance.

Existing lead-based perovskite solar cells have a problem in that their absorption spectrum is limited to the visible light region with a wavelength of 850 nanometers (nm) or less, which prevents them from utilizing approximately 52% of the total solar energy.

To solve this problem, the research team designed a hybrid device that combined an organic bulk heterojunction (BHJ) with perovskite and implemented a solar cell that can absorb up to the near-infrared region.

In particular, by introducing a sub-nanometer dipole interface layer, they succeeded in alleviating the energy barrier between the perovskite and the organic bulk heterojunction (BHJ), suppressing charge accumulation, maximizing the contribution to the near-infrared, and improving the current density (JSC) to 4.9 mA/cm².

The key achievement of this study is that the power conversion efficiency (PCE) of the hybrid device has been significantly increased from 20.4% to 24.0%. In particular, this study achieved a high internal quantum efficiency (IQE) compared to previous studies, reaching 78% in the near-infrared region.

< Figure. The illustration of the mechanism of improving the electronic structure and charge transfer capability through Perovskite/organic hybrid device structure and dipole interfacial layers (DILs). The proposed dipole interfacial layer forms a strong interfacial dipole, effectively reducing the energy barrier between the perovskite and organic bulk heterojunction (BHJ), and suppressing hole accumulation. This technology improves near-infrared photon harvesting and charge transfer, and as a result, the power conversion efficiency of the solar cell increases to 24.0%. In addition, it achieves excellent stability by maintaining performance for 1,200 hours even in an extremely humid environment. >

In addition, this device showed high stability, showing excellent results of maintaining more than 80% of the initial efficiency in the maximum output tracking for more than 800 hours even under extreme humidity conditions.

Professor Jung-Yong Lee said, “Through this study, we have effectively solved the charge accumulation and energy band mismatch problems faced by existing perovskite/organic hybrid solar cells, and we will be able to significantly improve the power conversion efficiency while maximizing the near-infrared light capture performance, which will be a new breakthrough that can solve the mechanical-chemical stability problems of existing perovskites and overcome the optical limitations.”

This study, in which KAIST School of Electrical Engineering Ph.D. candidate Min-Ho Lee and Master’s candidate Min Seok Kim participated as co-first authors, was published in the September 30th online edition of the international academic journal Advanced Materials. (Paper title: Suppressing Hole Accumulation Through Sub-Nanometer Dipole Interfaces in Hybrid Perovskite/Organic Solar Cells for Boosting Near-Infrared Photon Harvesting).

This study was conducted with the support of the National Research Foundation of Korea.

EE Prof. Yong-Hoon Kim’s team succeeded in accelerating calculations for electronic structure in quantum mechanics for the first time in the world using a convolutional neural network (CNN) model

< (from left): Prof. Yong-Hoon Kim, Ph.D. candidate Ryong Gyu Lee>

The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for devising the AI for their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated computer simulations for quantum mechanics by predicting atomic-level chemical bonding information distributed in 3D space using a novel approach to teach AI.

Professor Yong-Hoon Kim’s team from the School of Electrical Engineering developed a 3D computer vision artificial neural network-based computation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations traditionally performed using supercomputers to derive the properties of materials.

< Figure 1. Various methodologies are utilized in the simulation of materials and materials, such as quantum mechanical calculations at the nanometer (nm) level, classical mechanical force fields at the scale of tens to hundreds of nanometers, continuum dynamics calculations at the macroscopic scale, and calculations that mix simulations at different scales. These simulations are already playing a key role in a wide range of basic research and application development fields in combination with informatics techniques. Recently, there have been active efforts to introduce machine learning techniques to radically accelerate simulations, but research on introducing machine learning techniques to quantum mechanical electronic structure calculations, which form the basis of high-scale simulations, is still insufficient. >

The density functional theory (DFT) calculations in quantum mechanics using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow for fast and accurate prediction of quantum properties. *Density functional theory (DFT): A representative theory of ab initio (first principles) calculations that calculate quantum mechanical properties from the atomic level.

However, practical DFT calculations require generating 3D electron density and solving quantum mechanical equations through a complex, iterative self-consistent field (SCF)* process that must be repeated tens to hundreds of times. This restricts its application to systems with only a few hundred to a few thousand atoms. *Self-consistent field (SCF): A scientific computing method widely used to solve complex many-body problems that must be described by a number of interconnected simultaneous differential equations.

Professor Yong-Hoon Kim’s research team questioned whether recent advancements in AI techniques could be used to bypass the SCF process. As a result, they developed the DeepSCF model, which accelerates calculations by learning chemical bonding information distributed in a 3D space using neural network algorithms from the field of computer vision.

< Figure 2. The deepSCF methodology developed in this study provides a way to rapidly accelerate DFT calculations by avoiding the self-consistent field process (orange box) that had to be performed repeatedly in traditional quantum mechanical electronic structure calculations through artificial neural network techniques (green box). The self-consistent field process is a process of predicting the 3D electron density, constructing the corresponding potential, and then solving the quantum mechanical Cohn-Sham equations, repeating tens to hundreds of times. The core idea of the deepSCF methodology is that the residual electron density (δρ), which is the difference between the electron density (ρ) and the sum of the electron densities of the constituent atoms (ρ0), corresponds to chemical bonding information, so the self-consistent field process is replaced with a 3D convolutional neural network model. >

The research team focused on the fact that, according to density functional theory, electron density contains all quantum mechanical information of electrons, and that the residual electron density — the difference between the total electron density and the sum of the electron densities of the constituent atoms — contains chemical bonding information. They used this as the target for machine learning.

They then adopted a dataset of organic molecules with various chemical bonding characteristics, applying random rotations and deformations to the atomic structures of these molecules to further enhance the model’s accuracy and generalization capabilities. Ultimately, the research team demonstrated the validity and efficiency of the DeepSCF methodology on large, complex systems.

< Figure 3. An example of applying the deepSCF methodology to a carbon nanotube-based DNA sequence analysis device model (top left). In addition to classical mechanical interatomic forces (bottom right), the residual electron density (top right) and quantum mechanical electronic structure properties such as the electronic density of states (DOS) (bottom left) containing information on chemical bonding are rapidly predicted with an accuracy corresponding to the standard DFT calculation results that perform the SCF process. >

Professor Yong-Hoon Kim, who supervised the research, explained that his team had found a way to map quantum mechanical chemical bonding information in a 3D space onto artificial neural networks. He noted, “Since quantum mechanical electron structure calculations underpin property simulations at all scales, this research establishes a foundational principle for accelerating material calculations using artificial intelligence.”

Ryong-Gyu Lee, a PhD candidate in the School of Electrical Engineering, served as the first author of this research, which was published online on October 24 in Npj Computational Materials, a prestigious journal in the field of material computation. (Paper title: “Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints”)

This research was conducted with support from the KAIST Venture Research Program for Graduate and PhD Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.

EE Ph.D. candidate Edward Jongyoon Choi and Vincent Lukito from Professor Minkyu Je’s Lab Wins Corporate Special Award (Telechips) at the 25th Korea Semiconductor Design Challenge

EE Ph.D. candidate Edward Jongyoon Choi and Vincent Lukito from Professor Minkyu Je’s Lab

Wins Corporate Special Award (Telechips) at the 25th Korea Semiconductor Design Challenge

<(From left) Ph.D. candidate Edward Jongyoon Choi and Vincent Lukito>

Ph.D. candidate Edward Jongyoon Choi and Vincent Lukito from Professor Minkyu Je’s lab in EE was awarded the prestigious Corporate Special Award (Telechips) at the 25th Korea Semiconductor Design Challenge, held on October 24 at COEX, Seoul.

Organized by the Ministry of Trade, Industry and Energy and the Korea Semiconductor Industry Association, the Korea Semiconductor Design Challenge aims to cultivate the design capabilities of undergraduate and graduate students in the semiconductor field and to discover creative ideas that enhance the foundational competitiveness of the semiconductor industry.

The title of their award-winning research design is “Spike Sorting SoC with Delta-based Detection and Analog CIM-based Autoencoder Neural Network Feature Extraction Achieving 94.54% Accuracy,” in which both Ph.D. students participated.

The research was evaluated based on creativity, technical complexity, commercial viability, and completeness. Their project demonstrated outstanding merit in terms of innovative topics, high technical difficulty and excellence, potential for commercialization, and the completeness and validation of the work, receiving the Special Corporate Award (Telechips).

EE Professor Yoo Chang-Dong’s Lab Wins 1st Place in the 2024 SNUBH AKI Datathon

Doctoral students Jiwoo Hong, Kwanhyung Koo, and Seonjae Yoon, along with master’s student Younghwan Lee, from Professor Yoo Chang-Dong’s lab participated in the “2024 Bundang Seoul National University Hospital Acute Kidney Injury Datathon” under the team name “U-Vengers” and won the 1st Place Award.

This competition, hosted by Bundang Seoul National University Hospital, was an online datathon where participants used acute kidney injury (AKI) patient datasets to propose ideas and develop digital healthcare AI models.

The key goal was to create AI models that not only performed well but also demonstrated fairness across factors like gender and religion. The U-Vengers were recognized for the performance, fairness, creativity, and applicability of their developed model.

Details are as follows:

Event: 2024 Bundang Seoul National University Hospital Acute Kidney Injury Datathon

Overview: Participants used an AKI patient dataset to develop AI models for AKI prediction, applicable in real clinical settings. In the preliminary round, models were developed using the MIMIC-IV dataset, and in the final round, real data from Bundang Seoul National University Hospital was used to build practical models.

Competition Period: September 12 – October 20

Award: 1st Place (Director of Biomedical Research Institute Award, Bundang Seoul National University Hospital)

Participants: Jiwoo Hong (Team Leader), Kwanhyung Koo, Younghwan Lee, Seonjae Yoon

Prof. Kyeongha Kwon and Sang-Gug Lee Team had developed electrochemical impedance spectroscopy (EIS) technology

Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.

EE research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.

EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.

However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.

< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >

To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.

In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.

Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”

This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)

< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >

This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.

EE Professor Junil Choi Research Team Lead Development of New Visible Light Communication Encryption Technology Using Chiral Nanoparticles n collaboration with Seoul National University

EE Professor Junil Choi Research Team Lead Development of New Visible Light Communication Encryption Technology Using Chiral Nanoparticles n collaboration with Seoul National University

Recently, next-generation visible light communication technology, leveraging visible light’s high frequency and linear propagation used in lighting systems, has attracted significant interest. Visible light communication boasts high security and data transmission speed, but it remains vulnerable to eavesdropping due to signal leakage, necessitating further advancements in encryption. The novel approach by the research team aims to address this gap by harnessing the unique interaction between polarization and the chiral optical properties of nanoparticles, which significantly enhances encryption performance.

The collaborative research from KAIST and Seoul National University has successfully used chiral nanoparticles to develop a secure visible light communication technology that greatly improves security. They achieved this by leveraging the nanoparticles’ chiral optical properties.

The team demonstrated through simulations that the security of visible light communication can be enhanced by optimizing the polarization based on the chiral properties of the nanoparticles—properties that are exclusive to authorized receivers. This effectively blocks any eavesdropping attempts.

The research also revealed that signals passing through chiral nanoparticles create a unique differential channel due to circular dichroism—a phenomenon where the absorption of left- and right-handed circularly polarized light differs. The team found that adjusting the signal strength received through this differential channel can further boost encryption capabilities.

Furthermore, by comparing the bit error rates of legitimate receivers and potential eavesdroppers, the team demonstrated that visible light communication, once encrypted in this way, becomes nearly impossible to clone or intercept. They also showed that optimizing the polarization state based on the chiral properties allows for selective tuning of the system’s security and energy efficiency.

Professor Junil Choi emphasized, “This achievement was possible thanks to the collaboration between experts in materials science and electrical engineering. Moving forward, we intend to continue advancing visible light communication technology based on nanoparticles, aiming to develop a fundamentally eavesdropping-proof communication system.”

The study, co-authored by KAIST PhD candidate Gunho Han, Seoul National University PhD candidate Junghyun Han, and postdoctoral researcher Dr. Jiawei Liu, was published in the September issue of the prestigious multidisciplinary journal Nature Communications (Paper title: Spatiotemporally modulated full-polarized light emission for multiplexed optical encryption). This research was supported by the Agency for Defense Development through the Future Challenge Defense Technology Development Program.

Master’s student Jimin Lee from Professor Hyeon-Min Bae’s lab wins the Poster Excellence Award at the fNIRS 2024 Conference

Master’s student Jimin Lee from Professor Hyeon-Min Bae’s lab wins the Poster Excellence Award at the fNIRS 2024 Conference

Jimin Lee, a master’s student in Professor Hyeonmin Bae’s lab, was awarded the prestigious Poster Excellence Award at the fNIRS 2024 conference, held from September 11 to 15 at the University of Birmingham, UK.

Now in its 7th edition, fNIRS is a biennial international conference that brings together basic and clinical scientists focused on understanding the functional properties of biological tissues, including the brain.

The award-winning research poster, titled “Fiber-less Speckle Contrast Optical Spectroscopy System Using a Multi-Hole Aperture Method,” was a collaborative project involving Jimin Lee, Ph.D. students Seongkwon Yu and Bumjun Koh, and Master’s graduate Yuqing Liang.

This research was recognized by the fNIRS 2024 Program Committee for its excellence, earning the Poster Excellence Award, which is part of the Scientific Excellence Awards.

The award is given to master’s, Ph.D., and postdoctoral researchers who deliver outstanding posters or presentations, chosen from among the 350 posters presented at the conference.

KAIST EE’s Insue Won (MS, Graduated, 8. 2024), Jeoungmin Ji (Ph.D Candidate), and Donggyun Lee in Prof. Seunghyup Yoo’s lab awarded at the 2024 International Meeting on Information Display (IMID)]

[KAIST EE’s Insue Won (MS, Graduated, 8. 2024), Jeoungmin Ji (Ph.D Candidate), and Donggyun Lee in Prof. Seunghyup Yoo’s lab awarded at the 2024 International Meeting on Information Display (IMID)]

<(from left) Master’s Insue Won, Ph.D Candidate Jeoungmin Ji>

Insue Won (MS, Graduated, Aug., 2024) and Jeoungmin Ji (Ph.D Candidate) (Advised by Prof. Seunghyup Yoo) won the Best Poster Paper Award at the 2024 International Meeting on Information Display (IMID) for their work entitled “Temperature-Dependent Dynamics of Triplet Excitons in MR-TADF OLEDs: Insights from Magneto-Electroluminescence Analysis.”

In addition, Dr. Donggyun Lee (Ph.D. Graduated, Feb., 2024) won “Kim Yong-Bae Award Grand Prize” in IMID for his work on stretchable OLED displays.

The International Meeting on Information Display (IMID) is one of the world’s two largest international conferences in the field of display technology, held annually during the summer.

This year, the conference took place from August 20 to 23 at the Jeju Convention Center (ICC Jeju).

Ph.D candidate Jeoungmin Ji presented a poster titled “Temperature-Dependent Dynamics of Triplet Excitons in MR-TADF OLEDs: Insights from Magneto-Electroluminescence Analysis,” which was conducted in collaboration with Samsung Display and supported by the Technology Innovation Program funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea).

Additionally, Dr. Donggyun Lee was awarded the prestigious ‘Kim Yong Bae Award Grand Prize,’ which is presented annually at IMID to one graduate who submits an outstanding thesis in the field of display technology.

<Dr. Lee being awarded ‘Kim Yong Bae Award Grand Prize at IMID 2024>

Professor In-So Kweon Selected as Recipient of the 38th Inchon Prize in the Science and Technology Category

Professor In-So Kweon Selected as Recipient of the 38th Inchon Prize in the Science and Technology Category

Professor In-So Kwon has been selected as the recipient of the 38th Inchon Prize in the Science and Technology category, an award hosted by the Inchon Memorial Foundation and The Dong-A Ilbo.

The Inchon Memorial Foundation and The Dong-A Ilbo established the Inchon Prize in 1987 to honor the legacy of Inchon Kim Seong-Soo, who, during the turbulent period of Japanese colonial rule, founded The Dong-A Ilbo and Gyeongseong Textile Company, and nurtured talent through institutions such as Central School and Boseong Professional School (now Korea University).

Regarding Professor Kwon’s selection, the Inchon Memorial Foundation stated, “In the 1980s, when robotics and computer vision were largely unexplored fields in South Korea, Professor Kwon embarked on pioneering research that yielded world-class results. As a first-generation researcher in computer vision, he has trained over 200 students and laid the foundation for the AI computer vision field. Recently, he extended the ‘attention’ model, which simulates human focus, to computer vision. He also developed the CBAM algorithm, which significantly enhanced image recognition performance, with the related paper being cited over 20,000 times.”

Professor Kwon is a member of IEEE and has held key positions including Chair of the Department of Automation and Design Engineering at KAIST, Editorial Board Member of the International Journal of Computer Vision, Head of KAIST’s Robotics and Computer Vision Laboratory, Director of the KAIST P3 Digicar Center, Co-Chair of the 11th Asian Conference on Computer Vision, and President of the Korea Robotics Society in 2016.

Recipient Accouncment: https://www.donga.com/news/People/article/all/20240909/130005987/2
Recipients’ Acceptance Speech and Achievements: https://www.donga.com/news/Society/article/all/20240909/130005968/2

EE Professor Dongsu Han’s Research Team Develops Technology to Accelerate AI Model Training in Distributed Environments Using Consumer-Grade GPUs

EE Professor Dongsu Han’s Research Team Develops Technology to Accelerate AI Model Training in Distributed Environments Using Consumer-Grade GPUs

<(from left) Professor Dongsu Han, Dr. Hwijoon Iim, Ph.D. Candidate Juncheol Ye>

Professor Dongsu Han’s research team of the KAIST Department of Electrical Engineering has developed a groundbreaking technology that accelerates AI model training in distributed environments with limited network bandwidth using consumer-grade GPUs.

Training the latest AI models typically requires expensive infrastructure, such as high-performance GPUs costing tens of millions in won and high-speed dedicated networks.

As a result, most researchers in academia and small to medium-sized enterprises have to rely on cheaper, consumer-grade GPUs for model training.

However, they face difficulties in efficient model training due to network bandwidth limitations.

To address these issues, Professor Han’s team developed a distributed learning framework called StellaTrain.

StellaTrain accelerates model training on low-cost GPUs by integrating a pipeline that utilizes both CPUs and GPUs. It dynamically adjusts batch sizes and compression rates according to the network environment, enabling fast model training in multi-cluster and multi-node environments without the need for high-speed dedicated networks.

StellaTrain adopts a strategy that offloads gradient compression and optimization processes to the CPU to maximize GPU utilization by optimizing the learning pipeline. The team developed and applied a new sparse optimization technique and cache-aware gradient compression technology that work efficiently on CPUs.

This implementation creates a seamless learning pipeline where CPU tasks overlap with GPU computations. Furthermore, dynamic optimization technology adjusts batch sizes and compression rates in real-time according to network conditions, achieving high GPU utilization even in limited network environments.

Through these innovations, StellaTrain significantly improves the speed of distributed model training in low-cost multi-cloud environments, achieving up to 104 times performance improvement compared to the existing PyTorch DDP.

Professor Han’s research team has paved the way for efficient AI model training without the need for expensive data center-grade GPUs and high-speed networks. This breakthrough is expected to greatly aid AI research and development in resource-constrained environments, such as academia and small to medium-sized enterprises.

Professor Han emphasized, “KAIST is demonstrating leadership in the AI systems field in South Korea.” He added, “We will continue active research to implement large-scale language model (LLM) training, previously considered the domain of major IT companies, in more affordable computing environments. We hope this research will serve as a critical stepping stone toward that goal.”

The research team included Dr. Hwijoon Iim and Ph.D. candidate Juncheol Ye from KAIST, as well as Professor Sangeetha Abdu Jyothi from UC Irvine. The findings were presented at ACM SIGCOMM 2024, the premier international conference in the field of computer networking, held from August 4 to 8 in Sydney, Australia (Paper title: Accelerating Model Training in Multi-cluster Environments with Consumer-grade GPUs).

Meanwhile, Professor Han’s team has also made continuous research advancements in the AI systems field, presenting a framework called ES-MoE, which accelerates Mixture of Experts (MoE) model training, at ICML 2024 in Vienna, Austria.

By overcoming GPU memory limitations, they significantly enhanced the scalability and efficiency of large-scale MoE model training, enabling fine-tuning of a 15-billion parameter language model using only four GPUs. This achievement opens up the possibility of effectively training large-scale AI models with limited computing resources.

Archives: 연구 > 연구성과

EE Prof. Jung-Yong Lee research team developed a high-efficiency and high-stability organic-inorganic hybrid solar cell production technology that maximizes near-infrared light capture

EE Prof. Yong-Hoon Kim’s team succeeded in accelerating calculations for electronic structure in quantum mechanics for the first time in the world using a convolutional neural network (CNN) model

EE Ph.D. candidate Edward Jongyoon Choi and Vincent Lukito from Professor Minkyu Je’s Lab Wins Corporate Special Award (Telechips) at the 25th Korea Semiconductor Design Challenge

EE Professor Yoo Chang-Dong’s Lab Wins 1st Place in the 2024 SNUBH AKI Datathon

Prof. Kyeongha Kwon and Sang-Gug Lee Team had developed electrochemical impedance spectroscopy (EIS) technology

EE Professor Junil Choi Research Team Lead Development of New Visible Light Communication Encryption Technology Using Chiral Nanoparticles n collaboration with Seoul National University

Master’s student Jimin Lee from Professor Hyeon-Min Bae’s lab wins the Poster Excellence Award at the fNIRS 2024 Conference

KAIST EE’s Insue Won (MS, Graduated, 8. 2024), Jeoungmin Ji (Ph.D Candidate), and Donggyun Lee in Prof. Seunghyup Yoo’s lab awarded at the 2024 International Meeting on Information Display (IMID)]

Professor In-So Kweon Selected as Recipient of the 38th Inchon Prize in the Science and Technology Category

EE Professor Dongsu Han’s Research Team Develops Technology to Accelerate AI Model Training in Distributed Environments Using Consumer-Grade GPUs

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