Archives: 연구 > 연구성과

[Prof. Kayoung Lee]

 

KAIST EE Professor Kayoung Lee is selected for the Young Scholar Award at the 9th Korean Symposium on Graphene and 2D Materials, hold by the Korean Graphene Society. 

 

The Young Scholar Award is awarded to those who have made a great contribution to the Korean graphene and 2D materials field, among academics under the age of 40. 

 

Professor Kayoung Lee, as this year’s awardee, received the award with a prize of 1 million wons.

 

[Award ceremony picture, Society Chair, Prof. Jong-Hyun Anh, Prof. Kayoung Lee, from left ]

[Prof. Jae-Woong Jeong, Simok Lee, From Left ]

 

Ph.D. student Simok Lee (Advised by Jae-Woong Jeong) won the Best Paper Award at the 2022 Korean Sensors Society Autumn Conference.

The Korean Sensors Society Conference is a conference held every spring and fall, and this year, and this autumn conference was held in Yeosu from August 24th to 26th.

Ph.D. student Simok Lee has published a paper titled “Adaptive Electronic Skin with High Sensitivity and Large Bandwidth Based on Gallium Microdrop-Elastomer Composite”.

Details are follows. Congratulations once again to Ph.D. student Simok Lee and Professor Jae-Woong Jeong!

 

Conference: 2022 Korean Sensors Society Autumn Conference

 

Date: August 24-26, 2022

 

Award: Best Presentation Paper Award

 

Authors: Simok Lee, Sang-Hyuk Byun, Jae-Woong Jeong (Advisory Professor)

 

Paper Title: Adaptive Electronic Skin with High Sensitivity and Large Bandwidth based on Gallium Microdroplet-Elastomer Composite

 

 

EE Professor Rhu, Minsoo’s Research Team Build First-ever Privacy-aware Artificial Intelligence Semiconductor, Speeding Up the Differentially Private Learning Process 3.6 Times Google’s TPUv3

 

[Professor Rhu, Minsoo]

 

EE Professor Rhu and his research team have taken artificial intelligence semiconductors a big leap forward in the application of differentially private machine learning. Professor Rhu’s team analyzed the bottleneck component in the differentially private machine learning performance and devised a semiconductor chip greatly improving differentially private machine learning application performance.

Professor Rhu’s artificial intelligence chip consists of, among others, a cross-product-based arithmetic unit and an addition tree-based post-processing arithmetic unit and is capable of 3.6 times faster machine learning process compared with that of Google’s TPUv3, today’s most widely used AI processor.

The new chip also boasts comparable performance to that of NVIDIA’s A100 GPU, even with 10 times less resources.

 

[From left, Co-lead authors Park, Beomsik and Hwang, Ranggi; co-authors Yoon, Dongho and Choi, Yoonhyuk]

 

This work, with EE researchers Park, Beomsik and Hwang, Ranggi as co-first authors, will be presented as DiVa: An Accelerator for Differentially Private Machine Learning at the 55th IEEE/ACM International Symposium on Microarchitectures (MICRO 2022), the premier research venue for computer architecture research coming October 1 through 5 in Chicago, USA.

Professor Rhu’s achievements have been reported in multiple press coverage.

 

Links:

AI Times: http://www.aitimes.com/news/articleView.html?idxno=146435

Yonhap : https://www.yna.co.kr/view/AKR20201116072400063?input=1195m

Financial News : https://www.fnnews.com/news/202208212349474072

Donga Science : https://www.dongascience.com/news.php?idx=55893

Industry News : http://www.industrynews.co.kr/news/articleView.html?idxno=46829

[Professor Song Min Kim and first author Kang Min Bae, from left to right]

 

On the 28th, School of EE professor Song Min Kim’s research team has announced that they have succeeded in creating the world’s first mmWave backscatter system for massive IoT connectivity.

 

The research, (OmniScatter: extreme sensitivity mmWave backscattering using commodity FMCW radar), led by Kang Min Bae as first author, was presented at ACM MobiSys 2022 this June, and was presented with the best paper award. This is meaningful as it marks the second consecutive year in which the best paper award was presented to a paper belonging to a research group at KAIST’s School of Electrical Engineering.

 

The backscatter technology described by this research team can greatly reduce the maintenance cost as it operates on ultra-lower power of less than 10 μW, being able to run on a single battery for more than 40 years.

 

By enabling connectivity on a scale that far exceeds the network density required by next gen communication technologies such as 5G and 6G, this system may serve as a great potential for serving as a stepping stone for the upcoming hyperconnected era.

 

“mmWave backscattering is a dreamlike technology that can run IoT devices on a large scale, which can drive massive communications at ultra-low power compared to any other technology,” said Professor Song Min Kim. “We hope that this technology will be actively used for the upcoming era of Internet of Things,” he added.

 

The research was made possible by the funding from Samsung Future Technology Development Project and the Institute for Information & Communication Technology Planning & Evalution.

 

 

[Fig 1. Tags used for massive IoT communications (as depicted by red triangles). Over 1100 tags are able to communicate simultaneously without any conflicts]

 

 

 

KAIST EE Prof. Jang, Min Seok and his research team succeed in observing strongly confined mid-infrared light propagating on monocrystalline gold with a scattering-type scanning near-field optical microscope (s-SNOM). 

It is highly applicable in next-generation optoelectronic devices development, with increasing the interaction between light and matter by confining the light in an atomically flat nanostructure. The study will be useful in advancing high-efficient nanophotonics and quantum computing.

 

[From left, Prof. Jang, Min Seok and Research Prof. Sergey Menabde]

 

KAIST announced on the July 18th that a joint research effort has succeeded in implementing a new platform for strongly confined light propagation in a low-dimensional material. This finding is expected to contribute to the development of next-generation optoelectronic devices development based on strong light-matter interactions.

 

Stacking atomically flat two-dimensional materials results in van der Waals crystals, which exhibit properties different from the original two-dimensional materials. Phonon-polaritons are the composite quasi-particles resulting from the polar dielectric ions’ oscillations coupling with electromagnetic waves. In particular, phonon-polaritons forming in van der Waals crystals placed on highly conductive metals have a high degree of compression. This is because the charges in the polariton crystals reflect in the underneath metal due to the image charge effect, and thus produce a new kind of polariton called image phonon-polaritons.

 

Light propagating as image phonon-polaritons may induce strong light-matter interactions, but their propagation is limited on rough metal surfaces and thus suffers low feasibility.

 

Prof. Jang said, “This work illustrates very well the advantages of image polaritons, especially image phonon-polaritons. Image phonon-polaritons exhibit low loss and strong light-matter interactions useful in the development of next-generation optoelectronic devices.” He then added that he hopes to advance the commercialization of high-efficiency nanophotonic devices, including in metasurfaces, optical switches, and optical sensors.

 

This work, first-authored by Research Prof. Sergey Menabde, was published in Science Advances on the July 13th. It has been supported by Samsung Science & Technology Foundation and the National Research Foundation of Korea, as well as Korea Institute of Science and Technology, Japanese Ministry of Education, Culture, Sports, Science and Technology, and the Villum Foundation of Denmark.

 

 

Fig. 1 Nanotip used in measuring the image phonon-polaritons traveling in h-BN in super-high resolution

[Prof. Myoungsoo Jung, Miryeong Kwon , Donghyun Gouk, from left]

 

Title: EE Professor Yoo, Chang Dong and  Kweon, In So ’s Research Team Give Oral Presentation on Self-supervised Learning at ECCV 2022

KAIST EE Prof. Yoo, Chang Dong and Kweon, In So’s team conducted a joint research and proposed a self-supervised learning method that is remarkably robust and performs well with even only a small volume of labeled data.

<(From left) EE Professors Yoo, Chang Dong and Kweon, In So and Researchers Chaoning Zhang and Kang Zhang>

ECCV began in 1990 and has since focused on introducing the latest findings in artificial intelligence and machine learning research on vision and signal processing. It has long been a renowned conference on computer vision and deep learning, and its 2022 rendition gathered 5,803 submissions, only 1,650 (28%) of which have had the honor of being accepted, and merely a select 158 (2.7%) of the accepts given the opportunity for an oral presentation.

The team’s findings titled “Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness” earned the oral presentation honor and will be presented on Oct. 23, 2022 in Tel Aviv, Israel.

While artificial intelligence is making progress in various domains, it has yet to win full trust from humans. Reliable learning should encompass learning from little data as well as robust learning, and attempts at this objective have been made with combining self-supervised learning and adversarial learning. This work utilizes distillation methods to efficiently put together the two and proposed an adversarial learning framework capable of self-supervised learning without labels.

The paper outlining these findings has been selected as an ECCV Oral Presentation (acceptance rate 2.7%) work. The work is a joint endeavor by Professors Yoo, Chang Dong and Kweon, In So, and their team, and it promises exciting opportunities for providing high-performance services based on robust artificial intelligence learning from little data.

This research is supported by IITP by MSIT.

[Prof. Sung-Ju Lee, Jaemin Shin (KAIST PhD Candidate),  Prof. Yunxin Liu of  Tsinghua University , Prof. Yuanchun Li  of  Tsinghua University, from left]

 

The research team led by Prof. Sung-Ju Lee of KAIST has published a paper “FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients” at ACM MobiSys (International Conference on Mobile Systems, Applications, and Services) 2022. Founded in 2003, MobiSys has been a premier conference on Mobile Computing and Systems. This year, 38 out of 176 submitted papers have been accepted to be presented at the conference. 

 

Jaemin Shin (KAIST PhD Candidate) was the lead author, and this work was in collaboration with Tsinghua University in China (Professors Yuanchun Li and Yunxin Liu participated).

 

Federated Learning is a recent machine learning paradigm proposed by Google that trains on a large corpus of private user data without collecting them. The authors developed a systematic federated learning framework that accelerates the global learning process of federated learning. The new framework actively measures the contribution of each training sample of clients and selects the optimal samples to optimize the training process. The authors also included an adaptive deadline control scheme with varying training data, and achieved 4.5 times speedup in global learning process without sacrificing the model accuracy.

 

Prof. Lee stated that “Federated learning is an important technology used by many top companies. With the accelerated training time achieved by this research, it has become even more attractive for real deployments. Moreover, our technology has shown to work well in different domains such as computer vision, natural language processing, and human activity recognition.”