Researchers have proposed a novel system inspired by the neuromodulation of the brain, referred to as a ‘stashing system’, which 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 by artificial intelligence immitating 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 it as needed. The research group presented a new artificial intelligence learning method that directly implements these neural coordination circuit configurations. [1]

Figure 1. Energy-efficient AI hardware technology via a brain-inspired stashing system

Figure 1 shows 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. [2]

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. [4]

The stashing system used 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. [3]

References:

1. https://tipsaletips.com/energy-efficient-ai-hardware-technology-via-a-brain-inspired-stashing-system-sciencedaily/
2. https://www.sciencedaily.com/releases/2022/05/220517210435.htm
3. https://gadgets360.com/science/news/researchers-propose-new-energy-efficient-ai-hardware-technology-inspired-brain-neuromodulation-korea-advanced-institute-of-science-and-technology-2993224
4. https://www.2yodoindia.com/researchers-propose-energy-efficient-ai-hardware-technology-inspire-by-brains-neuromodulation-capacity/

Cite this article:

Thanusri swetha J (2022), Energy-efficient AI hardware technology via a brain-inspired stashing system, Anatechmaz, p.p 88