Brainpower inspires energy-efficient AI

Teresa Donnellan — Thu, 22 Jan 2026 16:57:20 +0000

Brainpower inspires energy-efficient AI Teresa Donnellan Thu, 01/22/2026 - 11:57

The key to energy-efficient artificial intelligence (AI) could lie in the human brain. �鶹��Ƶ researchers Maryam Parsa (PI) and Giorgio Ascoli (co-PI) are leading a Department of Energy-funded project to make AI dramatically more efficient by mimicking the brain’s computing strategies.

Maryam Parsa. Photo provided.

“AI today is powerful but extremely energy-hungry,” said Parsa, an assistant professor in George �鶹��Ƶ’s Department of Electrical and Computer Engineering who specializes in neuromorphic computing. “We want to understand why the human brain is so efficient and why AI is not—and then bridge that gap.”

Unlike traditional AI models that rely on continuous signals and massive data centers, the brain uses sparse, event-driven signals called spikes.

“Any one neuron only fires a spike here and there, but the timing of those spikes carries a lot of meaning,” explained Ascoli, a Distinguished University Professor in George �鶹��Ƶ’s Bioengineering Department specializing in neuroscience. “That’s why the brain can do so much with so little energy.”

To replicate this efficiency holistically, Parsa and Ascoli teamed up with Akhilesh Jaiswal at the University of Wisconsin–Madison and Pedram Khalili at Northwestern University. The team is building a full-stack neuromorphic computing system spanning devices, circuits, architectures, algorithms, and applications.

Each principal investigator is contributing specific expertise to the system:

Ascoli contributes neuroscience insights using Izhikevich models, mathematical frameworks that capture real neuron spiking dynamics, to inform system design.
Khalili develops spintronic devices, components that utilize magnetic materials to harness an electron's quantum properties of spin and charge for processing and storing data. By naturally exhibiting neuron-like temporal behavior, these devices are expected to reduce energy demands.
Jaiswal designs analog circuits to integrate these spintronic devices into functional architectures.
Parsa leads the project as well as neuromorphic learning algorithms and application-level integration, exploring diverse learning approaches to optimize speed, precision, resiliency, privacy, and energy savings.

Giorgio Ascoli. Photo provided.

“Izhikevich models allow neuromorphic systems to mimic the timing, diversity, and variability of real neurons, enabling more brain-like learning and efficiency,” Ascoli said. His extensive experience with these models, including simulating hippocampal networks, provides the biological foundation for the project.

“Instead of forcing existing devices to mimic brain behavior, we asked: What if we leverage new devices that naturally exhibit these dynamics?” Parsa said. This approach ensures innovations at the hardware level align with biologically inspired algorithms, creating a cohesive system rather than isolated solutions.

By introducing greater biological complexity and leveraging novel spintronic devices, the team hopes to reduce AI’s energy footprint while improving performance metrics such as accuracy and privacy. Early results are promising; even simple changes to spike distributions improved privacy without sacrificing accuracy.

The research also explores learning algorithms beyond the traditional. “Our goal is to see how different neuron models and learning rules interact and which combinations optimize for speed, precision, or energy savings,” Parsa said.

Ultimately, the project could transform AI by making it greener and more brain-like. “If we succeed, we’ll have systems that learn faster, consume less power, and operate more like the brain,” said Ascoli.

“This is not just about making AI more efficient,” said Parsa. “It’s about rethinking computing from the ground up."

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�鶹��Ƶ physicist receives university’s first DOE Early Career Award

ckearney — Wed, 25 Oct 2023 14:38:46 +0000

�鶹��Ƶ physicist receives university’s first DOE Early Career Award ckearney Wed, 10/25/2023 - 10:38

�鶹��Ƶ researcher Fereshte Ghahari Kermani recently received the highly competitive U.S. Department of Energy (DOE) Early Career Award. She is the first �鶹��Ƶ faculty member to receive this honor, one that highlights the significance of her contributions to the field of condensed matter physics.

This recognition supports scientists across the county whose work pushes the boundaries of scientific advancement and helps solidify the United States as a driver of science and innovation around the world.

Photo by Evan Cantwell/Office of University Branding

Ghahari’s study “Probing Correlated Phenomena in Graphene Constrictions” seeks to discover new pathways that will advance the development of novel quantum computing devices. To propel quantum technologies forward, Ghahari, who is an assistant professor in the Department of Physics and Astronomy, will look specifically at quantum electronic properties of charge carriers in graphene—a single atom thick layer of graphite—nanostructures.

Her research will address fundamental physics questions including whether a novel type of superconducting qubit can be designed by twisting graphene layers, as well as the properties of emergent particles of specific topological phases of matter. A qubit refers to a basic unit of quantum computing, such as the spin of an electron.

In search for these questions, Ghahari and her team will design and realize artificial potential landscapes in tiny graphene devices. Then they measure these devices at ultra-low temperatures and strong magnetic fields to probe correlated phases in their tunable dimensionalities.

“This research helps to develop new types of qubits by understanding specific quantum electron properties of particles when traveling through these constrictions,” Ghahari said.

Ghahari’s study was selected by the DOE’s Office of Basic Energy Sciences and will receive $875K over the course of five years. “It is nice to be recognized. This funding will support my career goals to make meaningful advances leading to the scientific insights required for the advancement of technologies based on quantum physics, “Ghahari said.

Ghahari’s past research has been in pursuit of understanding novel correlated and topological phenomena in graphene. She has made pioneering contributions to the field early in her career from the discovery of fractional quantum Hall effect and hydrodynamic thermal transport to Berry phase switch and quantum Hall wedding cakes in graphene nano devices.

Interested in Ghahari’s research? View another study currently in progress and her work recently published in Science.

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