Scientists design artificial synapses for neuromorphic computing

The human brain has been called the most complicated object in the universe. Seeking to replicate that as yet unmatched computing power, scientists at Los Alamos National Laboratory have built a new interface-like memristive device, which their results suggest could be used to build artificial synapses for next-generation neuromorphic computing.

Memristive devices, or memristors, represent a long-sought circuit technology that, unlike current resistor technology, has both programming and memory capabilities. and devices.

“Data processing is an essential part of science today, with machine learning, artificial intelligence and artificial neural networks being used to address pressing questions in everything from climate science to national security applications,” he said Aiping Chen, laboratory scientist at the Center for Integrated Nanotechnologies.

“But conventional computing architecture consumes a great deal of energy and is less and less able to scale to meet increasingly large data challenges. Neuromorphic computing, which mimics the unrivaled data storage and processing architecture and capabilities of the human brain, offers a path toward continuing to extend computing performance.”

Conventional computation is constrained by the so-called von Neumann bottleneck, where computation and memory are separated. Processing advanced tasks such as machine learning and image recognition on digital computers consumes a significant amount of energy and time due to data transfer back and forth between a central processing unit and memory. Data center energy consumption has increased rapidly in recent years, with projections that by 2030 around 8% of the world’s electricity will be used by data centers.

Also, in conventional computer architecture, billions of transistors on silicon-based microchips act as switches for a computer’s binary code. The physical limits on the miniaturization of those transistors helped spell the end of Moore’s Law, a maxim that computing power should double roughly every two years.

In-memory computing: just like a brain

By co-locating information storage and processing in the synapses, which connect the 100 billion neurons that send and receive chemical information, the human brain’s ‘in-memory processing’ saves time and energy. Neuromorphic computing relies on emerging devices such as memristors, switches between two terminals that control and remember flowing charge, to replicate the structure and function of synapses.

In the rapidly evolving field of neuromorphic computing, memristor designs have included filament systems, where a charge is delivered across devices. But, prone to overheating, filament systems lack stability and reliability.

Chen and his colleagues are working on a different approach called an interface-type memristor and have produced a reliable, high-performance device with a simple structure based on an Au/Nb-doped SrTiO₃ interface essentially gold and other semiconductor materials.

Interface-type memristors can, in principle, be reduced to nanometer sizes that even filament-based memristor technology cannot achieve. (By contrast, a human hair is about 100,000 nanometers thick.) And especially in contrast to transistor-based neuromorphic chips, the interface-type memristive device needs much less energy to power its processing.

“Different from digital computing with a von Neumann architecture, neuromorphic computing, inspired by biological systems, works just like a brain,” said Chen. “The advantages of this structure include low power consumption, high parallelism, and excellent fault tolerance. After all, the human brain runs on just 20 watts, but it learns extremely effectively. These advantages make it great for computational tasks such as learning, recognition and decision-making”.

Excel in advanced computing tasks

The team used artificial neural network simulation to study the computational performance of the interface-type memristor, testing it against a handwritten image dataset from the Modified National Standards and Technology database maintained by the National Institute of Standards and Technology. Demonstrating excellent uniformity, programmability, and reliability, the device achieved a recognition accuracy of 94.72%.

These performances lead the team to believe that these new interface-like memristive devices could be a critical hardware component for next-generation neuromorphic computing.

“The capabilities we’re seeing suggest that neuromorphic chips, like human brains, will be good at advanced tasks that include real-time learning and decision-making,” Chen said. “We could see the neuromorphic computer enabling many applications that require intelligence, from self-driving cars, to drones, to security cameras. Basically, a lot of things that people are capable of doing, these types of devices will be able to do. ”

The team plans to continue developing the technology with an emphasis on the need for co-engineering hardware design based on algorithmic approaches offered by computer scientists.