Physicists at the University of California, San Francisco receive $12.6 million from the Department of Energy for next-generation computing

September 29, 2022 – The first generation of computers used vacuum tubes. Second, transistors and third, integrated circuits. Each new generation has allowed computers to be faster, smaller, and more energy efficient. Now, as the world extends beyond the boundaries of integrated circuits, what does the fourth generation of computing look like?

Q-MEEN-C’s research seeks to simulate the emerging complexity that makes the brain an efficient computer.

The answer may lie in quantum materials capable of achieving neural, or brain-like, computing capabilities with low power consumption. Since 2018Quantum materials for energy-efficient neural computing.Q-MEEN-C) – led by the University of California, San Diego – was at the forefront of this research. Now, through a highly competitive process, the Department of Energy (DOE) has announced a $12.6 million funding renewal for the center.

UCSD Chancellor Pradeep K. “The center embodies many of our guiding principles for collaboration and pioneering research. This achievement reflects not only positively on the researchers, but also on the physics department and the entire university.”

Q-MEEN-C is one of the Department of Energy’s Frontier Energy Research Centers (EFRC) – one of more than 40 established to help meet the world’s most pressing energy technology challenges. The center, led by the University of California, San Diego, is a collaborative effort that brings together researchers from around the world. Each brings a unique experience to a compelling scientific challenge: creating a brain-like computer with dramatically lower power requirements.

Q-MEEN-C Director and Distinguished Physics Professor Evan K. “During the semiconductor revolution, materials science helped developers identify silicon and germanium as ideal materials. It is the same now, as we see quantum materials as the key to increasing computational power while also reducing local energy consumption.”

Quantum materials are a class of new materials that exhibit more complex quantum mechanical behavior than silicon, and whose combination of properties fits nicely with more efficient and transformative neural computing.

When Q-MEEN-C was created, the researchers set out to determine if quantum materials were viable even as energy-saving materials for neural computing. Over the past four years, they have successfully demonstrated that quantum materials have great potential due to their extraordinary electronic and magnetic properties.

Summary of key Q-MEEN-C metrics since 2018, including members and publications.

“This is only the beginning,” said center co-director and physics professor Alex Frañó. “Now that we’ve done Found viable materialWe are laying the groundwork for future research. The human brain is a network of neurons, synapses, and dendrites – you cannot have a brain-like computer without a brain-like network. We can take these quantum materials and combine them with other materials to see how they interact with each other as a step toward creating neural computing networks.”

Frañó says the center takes a holistic approach to the problem — 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 known as “emergence” – where the whole is greater than the sum of its parts, even if it is not clearly known how all the parts work together.

The primary goal in the development of neural computing is to recognize and classify patterns and learning Things the brain does well with minimal energy input. A person can see an image of the Golden Gate Bridge and an image of the Statue of Liberty and immediately differentiate between the two parameters. The computer will have to individually analyze billions of pixels in multiple images to reach the same result. Add fog, rain or a different angle and it gets more complicated.

While we see this ability to some extent when the photo software recognizes similar faces in the feed as the same person, there is a limit to how detailed the photos are, how long it takes to label them and how much power your phone battery requires.

“It’s not just a software problem,” Schuller stated. “You will not achieve energy efficiency through software improvements. There has to be a new type of hardware as well.”

One of the stated goals of the EFRCs is to train future energy scientists, and DOE funding supports students and postdoctoral scholars at Q-MEEN-C. “We are not only creating the next generation of knowledge, but also the next generation of researchers,” Frañó said. “One day our students will lead their own research groups on neural computing.”

“We are driven by a constant sense of astonishment. Neural computing probably won’t unfold the way we imagine it today, but it will unfold somehow. Decades from now could potentially go beyond what we can currently expect,” Schuller said. wonderful.”

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

source: Michelle Franklin, University of California

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