AI breakthrough: Laser neurons surging past biological speeds
Scientists from the University of Hong Kong have developed a laser neuron that operates a billion times faster than the biological nerve cells in our brains. This innovative chip promises to accelerate the performance of artificial intelligence further.
Researchers from China have created an artificial neuron using laser technology that mimics the functions of biological neurons in the brain while operating at significantly higher speeds). It emulates the dynamics and the characteristic information processing of living neurons, with speed surpassing natural cells by up to a billion times.
The new neuron processed data from 100 million heartbeats in a second
As the team of experts explained, living organisms have two types of neurons: those with a gradual response and those with an impulse response. This innovative solution focuses on gradual neurons, which process signals more precisely and circumvent the limitations of current photonic impulse structures.
The team leader, Chaoran Huang, emphasizes that the new laser neuron is characterized by exceptional dynamics and rapid data processing, making it well-suited for tasks related to pattern recognition or sequence prediction. This neuronal system's unique properties allow it to function like a miniature neural network, executing advanced tasks even within a single system.
In tests, the system demonstrated the capability to process enormous amounts of data in a fraction of a second. Laser-based neurons have processed data from 100 million heartbeats and nearly 35 million digital images in just one second.
Scientists: The potential of artificial neurons can be further increased
Prof. Huang, co-author of the publication, points out that utilizing cascaded laser neurons will offer even greater potential.
"In this work, we used a single laser graded neuron, but we believe that cascading multiple laser graded neurons will further unlock their potential, just as the brain has billions of neurons working together in networks," explains Prof. Huang, co-author of the publication.
