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Energy-Efficient AI Hardware Technology Via a Brain-Inspired Stashing System
Researchers 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 handle mathematical operations for artificial intelligence by imitating the continuous changes in the topology of the neural network according to the situation. The human brain changes its neural topology in real time, learning to store or recall memories as needed. The research group presented a new artificial intelligence learning method that directly implements these neural coordination circuit configurations. Research on artificial intelligence is becoming very active, and the development of artificial intelligence-based electronic devices and product releases are accelerating, especially in the Fourth Industrial Revolution age. To implement artificial intelligence in electronic devices, customized hardware development should also be supported. However most electronic devices for artificial intelligence require high power consumption and highly integrated memory arrays for large-scale tasks. It has been challenging to solve these power consumption and integration limitations, and efforts have been made to find out how the human brain solves problems. To prove the efficiency of the developed technology, the research group created artificial neural network hardware equipped with a self-rectifying synaptic array and algorithm called a ‘stashing system’ that was developed to conduct artificial intelligence learning. As a result, it was able to reduce energy by 37% within the stashing system without any accuracy degradation. This result proves that emulating the neuromodulation in humans is possible. Professor Kim said, "In this study, we implemented the learning method of the human brain with only a simple circuit composition and through this we were able to reduce the energy needed by nearly 40 percent.” This neuromodulation-inspired stashing system that mimics the brain’s neural activity is compatible with existing electronic devices and commercialized semiconductor hardware. It is expected to be used in the design of next-generation semiconductor chips for artificial intelligence. This study was published in Advanced Functional Materials in March 2022 and supported by KAIST, the National Research Foundation of Korea, the National NanoFab Center, and SK Hynix. -Publication: Woon Hyung Cheong, Jae Bum Jeon†, Jae Hyun In, Geunyoung Kim, Hanchan Song, Janho An, Juseong Park, Young Seok Kim, Cheol Seong Hwang, and Kyung Min Kim (2022) “Demonstration of Neuromodulation-inspired Stashing System for Energy-efficient Learning of Spiking Neural Network using a Self-Rectifying Memristor Array,” Advanced FunctionalMaterials March 31, 2022 (DOI: 10.1002/adfm.202200337) -Profile: Professor Kyung Min Kimhttp://semi.kaist.ac.kr https://scholar.google.com/citations?user=BGw8yDYAAAAJ&hl=ko Department of Materials Science and EngineeringKAIST
Professor Baik Awarded Sangsan Young Mathematician Prize
(Professor Hyungryul Baik) Professor Hyungryul Baik from the Department of Mathematical Sciences was honored as the recipient of the 2018 Sangsan Prize for Young Mathematicians by the Korean Mathematical Society (KMS). The Sangsan Prize recognizes young mathematicians who finished their degree within the previous five years and have begun an outstanding research career. Professor Baik was recognized for his studies in the fields of low-dimensional topology, geophysical mathematics, and geometric theory. In particular, his Ph.D. dissertation presented a new criterion that completely identifies the hyperbolic surface group, making an inference about the nature of the hyperbolic manifold group. Recently, Professor Baik co-published a paper entitled Spaces of Invariant Circular Orders of Groups with Professor Eric Samperton at the University of California Santa Barbara in the renowned academic journal Groups, Geometry, and Dynamics in 2018. Professor Baik earned his BS at KAIST and finished his MS and Ph.D. in mathematics in 2014 at Cornell University. He joined KAIST as a faculty member last year.
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