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      Researchers Propose Energy-Efficient AI Hardware Technology Inspire by Brain’s Neuromodulation Capacity

      Researchers have experimenting with many artificial intelligence technologies via devices and products. Implementing AI in electronic device requires customise hardware, which in turn result in high power consumption.

      It has been a challenge for researchers to solve issues relate to high power consumption that limits the integration of AI and electronic devices.

      In a recent, scientists at the Korea Advanced Institute of Science and Technology (KAIST) have come up with a new artificial intelligence system base on the activity of the human brain.

      The researchers were inspire by the brain’s capacity for neuromodulation, also known as the “stashing system”.

      They propose an AI network that constantly changes as per the situation on hand.

      The study was publish in the Advance Functional Materials journal and was support by KAIST, the National Research Foundation of Korea, the National NanoFab Center, and SK Hynix.

      The brain has the capacity to transform its neural topology as need, which allows it to store or recall memories as need.

      The researchers were inspire by this characteristic of the brain which they implement by the use of neural coordination circuit configurations in the AI learning method.

      The team, led by Professor Kyung Min Kim from the Department of Materials Science and Engineering at KAIST, came up with the hardware technology that can efficiently keep up AI mathematical operations by mimicking the brain through continuous changes in the topology of the neural network.

      Professor Kyung Min 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,”

      The new hardware is able to reduce energy consumption by 37% through the use of the stashing system and suffer no accuracy degradation.

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      The stashing system use by the researchers in the hardware consists of a self-rectifying synaptic array and algorithm.

      The system is also compatible with already existing electronic devices and the semiconductor hardware that is currently out on the market.

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