Nvidia Supplier SK Hynix Begins Mass Production of Next Generation Memory Chip 

The logo of SK Hynix is seen at its headquarters in Seongnam, South Korea, April 25, 2016. (Reuters)
The logo of SK Hynix is seen at its headquarters in Seongnam, South Korea, April 25, 2016. (Reuters)
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Nvidia Supplier SK Hynix Begins Mass Production of Next Generation Memory Chip 

The logo of SK Hynix is seen at its headquarters in Seongnam, South Korea, April 25, 2016. (Reuters)
The logo of SK Hynix is seen at its headquarters in Seongnam, South Korea, April 25, 2016. (Reuters)

SK Hynix Inc said on Tuesday it has begun mass production of next-generation high-bandwidth memory (HBM) chips used in artificial intelligence chipsets, with sources saying initial shipments will go to Nvidia this month.

The new type of chip - called the HBM3E - is a focal point of intense competition. Last month, Micron Technology said it had started mass production of the chips while Samsung Electronics said it had developed the industry's first 12-stack HBM3E chips.

SK Hynix has, however, led the HBM chip market by virtue of being the sole supplier of the version currently used - the HBM3 - to Nvidia which has 80% of the market for AI chips.

"The company expects successful mass production of HBM3E and with our experience... as the industry's first provider of HBM3, we expect to cement our leadership in the AI memory space," SK Hynix said in a statement.

The new HBM3E chip by the world's second-largest memory chipmaker offers 10% improvement in heat dissipation and processes up to 1.18 terabytes of data per second.

SK Hynix's HBM capacity is fully booked for 2024, analysts said, as explosive demand for AI chipsets drives up demand for high-end memory chips used in them.

"SK Hynix has secured an absolute market position... and its volume increase in high-end memory chips is also expected to be the most aggressive among chipmakers," said Kim Un-ho, analyst at IBK Investment & Securities.

Nvidia unveiled on Monday its latest flagship AI chip, the B200, said to be 30 times speedier at some tasks than its predecessor as it seeks to maintain its dominant position in the artificial-intelligence industry.

Shares in SK Hynix have doubled in value over the past 12 months on its leading position in HBM chips.



SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI
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SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

The Center of Excellence for Data Science and Artificial Intelligence at the Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have introduced the MiniGPT-Med model.

The large multi-modal language model is designed to help doctors quickly and accurately diagnose medical radiology using artificial intelligence techniques.

Dr. Ahmed Alsinan, the Artificial Intelligence Advisor at the National Center for Artificial Intelligence and head of the scientific team at SDAIA, explained that the MiniGPT-Med model is capable of performing various tasks such as generating medical reports, answering medical visual questions, describing diseases, locating diseases, identifying diseases, and documenting medical descriptions based on entered medical images.

The model was trained on different medical images, including X-rays, CT scans, and MRIs.

The MiniGPT-Med model, derived from large-scale language models, is specifically tailored for medical applications and demonstrates significant versatility across different imaging methods, including X-rays, CT scans, and MRI. This enhances its utility in medical diagnosis.

Dr. Alsinan highlighted that the MiniGPT-Med model was developed collaboratively by artificial intelligence specialists from SDAIA and KAUST.

The model exhibits advanced performance in generating medical reports, achieving 19% higher efficiency than previous models. It serves as a general interface for radiology diagnosis, enhancing diagnostic efficiency across various medical imaging applications.