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Reconfigurable AI Device Shows Brainlike Promise

New perovskite-based hardware can turn itself into all the key components needed for AI

2 min read
Purple gloved fingers hold a small, square, thin, golden device.
Purdue University

An adaptable new device can transform into all the key electric components needed for artificial-intelligence hardware, for potential use in robotics and autonomous systems, a new study finds.

Brain-inspired or "neuromorphic" computer hardware aims to mimic the human brain's exceptional ability to adaptively learn from experience and rapidly process information in an extraordinarily energy-efficient manner. These features of the brain are due in large part to its plastic nature—its ability to evolve its structure and function over time through activity such as neuron formation or "neurogenesis."

"We hypothesized if we could mimic these neurogenesis behaviors in electrical hardware, we could make machines that learn throughout their life-spans," says study senior author Shriram Ramanathan, an electrical engineer and materials scientist at Purdue University, in West Lafayette, Ind.

Now Ramanathan and his colleagues have developed highly adaptable neuromorphic devices using perovskite nickelate. Perovskites are crystals that have led to phenomenal advances in solar cell performance in the past decade or so.

The scientists incorporated protons into perovskite nickelate. Electric pulses applied to this material could shuffle the protons around within the material's lattice, altering its electronic properties. The researchers could electrically reconfigure a device made from this proton-doped perovskite nickelate into a resistor, a memory capacitor, a neuron, or a synapse on demand.

Schematic of perovskite nickelate electronic device. Multiple electronic functions can be reconfigured in a single neuromorphic device. Michael Park/Purdue University

"We can use one single device to perform multiple neuromorphic functions," says study colead author Hai-Tian Zhang, an electrical engineer and materials scientist at Purdue. "On top of that, we can switch among these functions with simple nanosecond-time-scale electric pulses."

The versatility of this device "could simplify AI circuit design for complex computational tasks by avoiding an agglomeration of different functional units that are area- and power-consuming," says study colead author Michael Tae Joon Park, an electrical engineer and materials scientist at Purdue. Potential applications include robotics and autonomous systems, he notes.

In simulations using the new device in an artificial neural network, which mimics the structure of neurons in biological brains, the scientists found that the reconfigurable nature of the new device enabled the neural network "to make its decisions more efficiently, compared to conventional static networks, in complex and ever-changing environments," Zhang says.

The new device proved stable over 1.6 million cycles of switching between states. "Also, hydrogen ions remain in the device for a long period of time after its initial treatment—over six months—which is encouraging," Park says.

The researchers suggest their device could find use in grow-when-required networks, which are neural networks that can grow their computing power on demand. Similarly, such networks can shrink in size if the device detects nodes that are regularly inactive in order to become more efficient.

The scientists note that they fabricated their devices using semiconductor-foundry-compatible techniques, suggesting they might readily find use within the electronics industry. However, "the status of our research is in its infancy," Zhang says. "Much more work is required to fabricate large-scale integrated test circuitry with these devices."

The scientists detailed their findings in a recent issue of the journal Science.

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Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

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