Young-Gyu+Yoon
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KAIST Develops Neuromorphic Semiconductor Chip that Learns and Corrects Itself
< Photo. The research team of the School of Electrical Engineering posed by the newly deveoped processor. (From center to the right) Professor Young-Gyu Yoon, Integrated Master's and Doctoral Program Students Seungjae Han and Hakcheon Jeong and Professor Shinhyun Choi >
- Professor Shinhyun Choi and Professor Young-Gyu Yoon’s Joint Research Team from the School of Electrical Engineering developed a computing chip that can learn, correct errors, and process AI tasks
- Equipping a computing chip with high-reliability memristor devices with self-error correction functions for real-time learning and image processing
Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.
KAIST (President Kwang Hyung Lee) announced on the 17th of January that the joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed a next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own.
< Figure 1. Scanning electron microscope (SEM) image of a computing chip equipped with a highly reliable selector-less 32×32 memristor crossbar array (left). Hardware system developed for real-time artificial intelligence implementation (right). >
What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.
This self-learning ability has been proven by achieving accuracy comparable to ideal computer simulations in real-time image processing. The research team's main achievement is that it has completed a system that is both reliable and practical, beyond the development of brain-like components.
The research team has developed the world's first memristor-based integrated system that can adapt to immediate environmental changes, and has presented an innovative solution that overcomes the limitations of existing technology.
< Figure 2. Background and foreground separation results of an image containing non-ideal characteristics of memristor devices (left). Real-time image separation results through on-device learning using the memristor computing chip developed by our research team (right). >
At the heart of this innovation is a next-generation semiconductor device called a memristor*. The variable resistance characteristics of this device can replace the role of synapses in neural networks, and by utilizing it, data storage and computation can be performed simultaneously, just like our brain cells.
*Memristor: A compound word of memory and resistor, next-generation electrical device whose resistance value is determined by the amount and direction of charge that has flowed between the two terminals in the past.
The research team designed a highly reliable memristor that can precisely control resistance changes and developed an efficient system that excludes complex compensation processes through self-learning. This study is significant in that it experimentally verified the commercialization possibility of a next-generation neuromorphic semiconductor-based integrated system that supports real-time learning and inference.
This technology will revolutionize the way artificial intelligence is used in everyday devices, allowing AI tasks to be processed locally without relying on remote cloud servers, making them faster, more privacy-protected, and more energy-efficient.
“This system is like a smart workspace where everything is within arm’s reach instead of having to go back and forth between desks and file cabinets,” explained KAIST researchers Hakcheon Jeong and Seungjae Han, who led the development of this technology. “This is similar to the way our brain processes information, where everything is processed efficiently at once at one spot.”
The research was conducted with Hakcheon Jeong and Seungjae Han, the students of Integrated Master's and Doctoral Program at KAIST School of Electrical Engineering being the co-first authors, the results of which was published online in the international academic journal, Nature Electronics, on January 8, 2025.
(Paper title: Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array, https://doi.org/10.1038/s41928-024-01318-6)
This research was supported by the Next-Generation Intelligent Semiconductor Technology Development Project, Excellent New Researcher Project, PIM AI Semiconductor Core Technology Development Project, and the Electronics and Telecommunications Research Institute Research and Development Support Project of the National Research Foundation of Korea.
2025.01.17 View 414 -
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
2022.06.22 View 7848