UCSD Physicists Receive $12.6M From DOE for Next-Gen Computing

September 29, 2022 — The first generation of computers used vacuum tubes. The second, transistors and the third, integrated circuits. With each new generation, computers have become faster, smaller and more energy efficient. Now that the world is moving beyond the limits of integrated circuits, what does the fourth generation of computers look like?

Q-MEEN-C research attempts to mimic the evolving complexity that makes the brain an efficient computer.

The answer could lie in quantum materials capable of achieving neuromorphic or brain-like computing abilities with low energy consumption. Since 2018, Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) – led by the University of California San Diego – has been at the forefront of this research. Now, in a hard-fought process, the Department of Energy (DOE) has announced $12.6 million in renewed funding for the center.

“This additional round of funding is a testament to the Department of Energy’s confidence in the work of Q-MEEN-C,” said Pradeep K. Khosla, Chancellor of UC San Diego. “The center embodies many of our guiding principles of collaboration and cutting-edge research. This achievement not only has a positive impact on the researchers, but also on the Department of Physics and the entire university.”

Q-MEEN-C is a DOE Energy Frontier Research Center (EFRC) – one of more than 40 established to help address some of the most pressing energy technology challenges. Led by UC San Diego, the center is a collaborative effort that brings together researchers from around the world. They all bring unique expertise to a compelling scientific challenge: creating a brain-like computer that uses drastically less power.

“With current technology, the local energy requirement to produce a device that mimics the brain is so great that it is impractical,” said Ivan K. Schuller, director of Q-MEEN-C and distinguished professor of physics. “During the semiconductor revolution, materials science helped designers identify silicon and germanium as ideal materials. It is the same now that we see quantum materials as the key to increasing computational power while reducing local energy consumption.”

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Quantum materials are a class of new materials that exhibit more complex quantum mechanical behavior than silicon and whose property spectrum is well suited for more efficient and transformative neuromorphic computing.

When Q-MEEN-C was founded, the researchers wanted to determine whether quantum materials were even suitable as an energy-efficient material for neuromorphic computing. Over the past four years, they have successfully shown that quantum materials have great potential due to their unusual electronic and magnetic properties.

A summary of Q-MEEN-C’s key metrics since 2018, including members and publications.

“This is just the beginning,” said Alex Frañó, the center’s co-director and professor of physics. “Now that we have found useful materials, we are laying the groundwork for future research. The human brain is a network of neurons, synapses and dendrites – there is no brain-like computer without a brain-like network. We can take these quantum materials and combine them with other materials to see how they react with each other as a step towards creating neuromorphic computing networks.”

Frañó says the center approaches the problem holistically – from a single electron in an atom to the complexity of a computer chip: “You have to understand the physics at every stage.” This is called “emergence” – where the whole is more than the sum of its parts, even if it is not explicitly known how all parts work together.

A major goal in the development of neuromorphic computing is pattern recognition, classification, and learning—things the brain does remarkably well with minimal expenditure of energy. A human could see an image of the Golden Gate Bridge and an image of the Statue of Liberty and immediately distinguish between the two landmarks. A computer would have to analyze the billions of pixels in multiple images individually to arrive at the same result. Add fog or rain or any other angle and it gets even more complicated.

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While we do see this ability to some extent when photo software recognizes similar faces in a feed as the same person, there is a limit based on how detailed the images are, how long it takes to categorize them, and how much Energy it needs your phone battery.

“It’s not just a software issue,” Schuller explained. “Energy efficiency cannot be achieved with software improvements. There also has to be a new kind of hardware.”

One of the stated goals of the EFRCs is to educate future energy scientists, and the DOE grant supports undergraduate and post-doctoral students at the Q-MEEN-C. “Not only are we creating the next generation of knowledge, we are also creating the next generation of researchers,” said Frañó. “One day our students will lead their own research groups on the topic of neuromorphic computing.”

“We are motivated by a continuous sense of wonder. Perhaps neuromorphic computing will not unfold in the way we envision it today, but it will unfold somehow. It may be decades away and it will likely exceed what we can currently predict,” Schuller said. “But this next generation — they’re going to see something wonderful.”

Funding by DOE #DE-SC0019273. A full list of principal investigators and participating institutions can be found on the Q-MEEN-C website.

Source: Michelle Franklin, UCSD