In a breakthrough that could usher in a new era of artificial intelligence (AI), scientists at the US National Institute of Standards and Technology (NIST) in Colorado have created a neuromorphic superconducting switch that “learns” to mimic neuro-biological architectures present in the human brain.
In a paper published in Science Advances on January 26, the NIST research team headed by physicist Mike Schneider, said the synthethic switch they called a ‘superconducting synapse’ – just like the structure in the brain that permits neurons to communicate with each other – has been shown to process synapse-like properties in a completely inorganic device. Researchers also note the new neuromorphic hardware, which has the shape of a metallic cylinder that’s 10 micrometres in diameter, is designed to process incoming flows of electricity it receives and produce appropriate output signals.
NIST further explains that multiple artificial synapses could be 3D stacked so that three-dimensional interconnectivity, high-device density and flexibility can be achieved. Another advantage of this technique is that by processing multi-dimensional information through a fault-tolerant computation process or learning through experience – or even from the surrounding environment, synthetic synapses, which can also connect processors and store memories of neuromorphic systems in units of magnetic flux, could offer a viable solution to mitigate the barriers of interconnect scaling in advanced computer systems.
Furthermore, the new neuromorphic hardware thanks to its superconducting ability to transmit electricity without resistance, is reportedly way faster in firing signals than human neurons. Apparently, the artificial synapse is capable of firing 1 billion times per second, while a brain cell is limited to 50x per second. This has a significant impact on neuromorphics processing because the higher the bursts of electric current that can be sent and received – the stronger the connection between the synapses become. Additionally, the new synthetic synapse requires far less power to process AI algorithms.
What makes NIST’s new invention a significant breakthrough, though, is that it supplies a missing link for so-called neuromorphic computers — computers that function like the human brain. Envisioned as a new type of artificial intelligence, such lightning fast decision-making, low-energy-need computers could be vital to improving the perception and decision-making for various uses, including medical diagnoses, self-driving cars and intelligence analysis.
NIST’s invention clearly marks a fresh approach to neuromorphic computing. However, Schneider believes that while the artificial synapse could lead to the creation of a more complex neuromorphic computer than has been developed using other technologies, the practical applications of the emerging tech that does everything its organic counterpart can do, but better, are relatively far in the future.
“We have demonstrated device-level performance that is very interesting, but a substantial amount of work remains to scale this technology for fielding, ” Schneider said, noting that we are “five to 10 years away from something in the field.”