Amazon’s Alexa. Google’s Nest. Facebook feeds. Smartphones. The principles of artificial intelligence are seamlessly woven into our daily lives, but we have only scratched the tip of the iceberg.

What if the game-changing technology could be leveraged in a much broader range of applications that could vary from personal assistants to swarms of drones? This is the essential premise that forms the foundation for the Center for Brain-Inspired Computing (C-BRIC), a five-year project supported by $31 million in funding from the Semiconductor Research Corporation’s JUMP program. This program is supported by both industry powerhouses, such as IBM and Intel, as well as U.S. federal funding through DARPA.

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As semiconductor device sizes shrink, chip design software is increasingly flying blind. With nanoscale-sized components, performance often hinges on the precise arrangement of atoms and the predictable behavior of electrons or phonons (vibrations) passing through their atomic structures. Yet, today’s commercial device design software treats matter as a continuum and views electrons only as classical particles.

“The number of atoms in today’s transistors are so small they have become countable,” says Gerhard Klimeck, director of the Network for Computational Nanotechnology, Reilly Director of the Center for Predictive Materials and Devices, and professor of electrical and computer engineering. The 22nm-nanometer fabricated Intel transistors that fuel entry-level desktop computers, for example, have silicon feature sizes that are only 8nm thick and 64 atoms wide. With the latest 14nm and 10nm transistors, some critical layer thicknesses are as small as 10 atoms.

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Emerging advances stem from the realization that deep neural networks, a type of deep-learning architecture, could be much more powerful by using many layers of processing.

“I have never seen anything so disruptive and transformational as deep learning,” says Charles Bouman, Purdue’s Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering.

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“The brain itself is the ultimate intelligent machine,” says Zhongming Liu, assistant professor of biomedical engineering and electrical and computer engineering. “It constantly processes information, makes decisions and guides actions. Although it is not fully understood how the brain works, decades of neuroscience research have already fueled inspiration for engineers to design algorithms that have enabled machines to understand images, translate language, play games, and drive vehicles, to name a few examples. Brain-inspired algorithms have also begun to help neuroscientists understand the brain itself and eventually decode the human mind.”

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The immediate goal of the project is to use advanced sensing coupled with machine-learning techniques and biophysical modeling for high throughput phenotyping, with the longer-term goal of actually connecting the phenotypes to specific genes.

The project is a partnership led by researchers in Purdue’s Colleges of Agriculture and Engineering, the Purdue Polytechnic Institute, IBM Research, and The University of Queensland. It is funded with a three-year, $6.5 million grant from the U.S. Department of Energy’s Advanced Research Projects Agency for projects focused on accelerating energy crop development for the production of renewable transportation fuels.

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