Energy-efficient AI hardware technology via a brain-inspired stashing system? – Science Daily

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.
Story Source:
Materials provided by The Korea Advanced Institute of Science and Technology (KAIST). Note: Content may be edited for style and length.
Journal Reference:
Cite This Page:
Visit New Scientist for more global science stories >>>
Get the latest science news with ScienceDaily’s free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:
Keep up to date with the latest news from ScienceDaily via social networks:
Tell us what you think of ScienceDaily — we welcome both positive and negative comments. Have any problems using the site? Questions?

source

Leave a Reply

Next Post

David Walden, Computer Scientist at Dawn of Internet, Dies at 79 - The New York Times

Wed May 25 , 2022
AdvertisementSupported byIn 1969, he was part of a team of young engineers who built the first machine to switch data among computers using the Arpanet, the precursor to the internet.Send any friend a storyAs a subscriber, you have 10 gift articles to give each month. Anyone can read what you […]
%d bloggers like this: