Engineers at RMIT University have developed a small ‘neuromorphic’ device that processes visual signals like the human brain—detecting movement, storing memories and responding instantly, all without relying on external computers.
This innovation could significantly improve how advanced communication systems handle visual information, creating faster and more efficient responses in devices that connect people and machines.
Professor Sumeet Walia, Director of RMIT’s Centre for Opto-electronic Materials and Sensors (COMAS), explained that this new device mirrors the brain’s analogue processing to reduce energy use while handling complex visual tasks. “Neuromorphic vision systems are designed to use similar analogue processing to our brains, which can greatly reduce the amount of energy needed to perform complex visual tasks compared with digital technologies used today,” he said.
The device is built using molybdenum disulfide (MoS2), a metal compound with unique atomic-scale defects. These defects allow the chip to capture light and convert it directly into electrical signals — much like neurons in the brain transmit information.
This ability to process visual data instantly and efficiently has major implications for communication technologies, especially where speed and energy efficiency are crucial.
“Current digital systems, by contrast, are very power hungry and unable to keep up as data volume and complexity increases, which limits their ability to make ‘true’ real-time decisions,” Walia said.
During testing, the chip demonstrated an impressive capacity for edge detection — recognising movement, such as a waving hand, without needing to capture every frame. It could then store these changes as memories, similar to how the brain retains visual cues. Such capability could improve real-time visual communication between humans and machines, reducing lag and boosting responsiveness.
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RMIT PhD scholar Thiha Aung, lead author on the research, noted, “We demonstrated that atomically thin molybdenum disulfide can accurately replicate the leaky integrate-and-fire (LIF) neuron behaviour, a fundamental building block of spiking neural networks.”
The research, published in Advanced Materials Technologies, opens doors to new communication applications beyond traditional digital processing.
As social media and real-time video platforms demand faster and more efficient handling of visual data, neuromorphic chips like this could transform how devices process and respond to visual signals.
Professor Akram Al-Hourani, Deputy Director of COMAS, said, “For robots working closely with humans in manufacturing or as personal assistants, neuromorphic technology could enable more natural interactions by recognising and reacting to human behaviour with minimal delay.”
The team is now expanding the device from a single pixel to a larger array, supported by an Australian Research Council grant. They aim to integrate this analogue neuromorphic technology with existing digital systems to improve communication technologies where energy efficiency and rapid real-time responses are essential.
Walia added, “We see our work as complementary to traditional computing, rather than a replacement. Conventional systems excel at many tasks, while our neuromorphic technology offers advantages for visual processing where energy efficiency and real-time operation are critical.”
The team is also exploring other materials to extend sensing capabilities into infrared, which could help track emissions and detect toxic gases or pathogens in real time.
