Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia used a spiking neural network (SNN) on a microchip as a foundation for developing more efficient hardware-based artificial intelligence systems.
KAUST's Wenzhe Guo said SNNs mimic the biological nervous system and can process information faster and more efficiently than artificial neural networks.
The researchers created a brain-on-a-chip using a standard FPGA microchip and a spike-timing-dependent plasticity model, which allowed the neuromorphic computing system to learn real-world data patterns without training.
Compared to other neural network platforms, the brain-on-a-chip was more than 20 times faster and 200 times more energy efficient.
Guo said, "Our ultimate goal is to build a compact, fast and low-energy brain-like hardware computing system."
From KAUST Discovery (Saudi Arabia)
Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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