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Brain-like machines will make smart grids smarter and self-driving cars safer—while saving energy and calculating at higher speeds

Illustration by Ryan Farrell, Division of Research

Designing and building computers that imitate the human brain is the goal of the first Energy Frontier Research Center led by Texas A&M University and funded by the US Department of Energy.

The new center will focus on Reconfigurable Electronic Materials Inspired by Nonlinear Neuron Dynamics (REMIND), an initiative that strives to transform computing to behave more like a human brain for rapid and efficient processing.

Collaborators on the center’s interdisciplinary research include the College of Engineering, the Department of Chemistry, the Texas A&M Engineering Experiment Station, the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory and Sandia National Laboratories.

R. Stanley Williams, professor in the Department of Electrical and Computer Engineering, will serve as the director of the EFRC, and Sarbajit Banerjee, professor in the Department of Materials Science & Engineering and the Department of Chemistry, will serve as the associate director.

“We are at a crossroads for the future of computing,” said Banerjee. “Self-driving cars, networked grids and personalized medicine are on the rise, all of which require massive amounts of energy. A whole new approach that focuses on brain-like computing is essential to meet the needs of society.” 

Modern computers excel at various essential functions, like high-precision arithmetic and solving known equations. However, they perform poorly when it comes to natural human abilities such as real-time learning, concept identification and decision making.

This ability to process information is possible because human brains have nerve cells (neurons) that continuously compare incoming stimuli with previously learned data. Neurons communicate with one another via electrical and chemical signals through connections called synapses that store memories. Although the individual biological steps are slow compared to those in transistors, enormous numbers of them operate simultaneously to perform sophisticated computation with energy-efficient orders of magnitude superior to the most advanced electronic computers. 

“Let’s say we are looking at a picture of a dog,” said Banerjee. “A human brain can almost immediately recognize the dog itself, its type and relative age. A computer will struggle with the basic recognition and may make a significant mistake while also using much more energy to do so.” 

The researchers involved with the REMIND initiative are discovering ways to emulate human neurons and synapses in electrical circuits by designing, creating and assembling materials that exhibit tunable nonlinear responses to incoming electrical signals, such as thresholding, amplification, integration and embedded memory. In other words, they are emulating the human brain’s processing system and attempting to assemble it into a highly efficient and capable computer. 

In addition to Banerjee and Williams, REMIND researchers include Raymundo Arroyave, Matt Pharr, Xiaofeng Qian and Patrick Shamberger from the materials science and engineering department; Perla Balbuena from the Artie McFerrin Department of Chemical Engineering; and Marcetta Darensbourg and Kim Dunbar from the Department of Chemistry.