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KAIST (President Kwang Hyung Lee) is a high-speed, low-power artificial intelligence (AI: Artificial Intelligent) semiconductor* MetaVRain, which implements artificial intelligence-based 3D rendering that can render images close to real life on mobile devices. * AI semiconductor: Semiconductor equipped with artificial intelligence processing functions such as recognition, reasoning, learning, and judgment, and implemented with optimized technology based on super intelligence, ultra-low power,
2023-03-13- As the marriage of AI and semiconductor being highlighted as the strategic technology of national enthusiasm, KAIST's achievements in the related fields accumulated through top-class education and research capabilities that surpass that of peer universities around the world are standing far apart from the rest of the pack. As Artificial Intelligence Semiconductor, or a system of semiconductors designed for specifically for highly complicated computation need for AI to conduct its learning a
2022-08-05KAIST breaks new grounds in positioning technology with an AI-integrated GPS board that works both indoors and out KAIST (President Kwang Hyung Lee) announced on the 8th that Professor Dong-Soo Han's research team (Intelligent Service Integration Lab) from the School of Computing has developed a GPS system that works both indoors and outdoors with quality precision regardless of the environment. This Indoor/Outdoor-Integrated GPS System, or IOI GPS System, for short, uses the GPS signals o
2022-07-13Simultaneous 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 neur
2022-05-20Researchers 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 h
2022-05-18