The human brain is among the most efficient, sophisticated systems in the known universe, deftly handling pattern and speech recognition, information processing, and even power consumption.

Plus, it weighs only about three pounds and fits inside a skull.

“The brain is one of the best templates available for big-data analysis and classifications,” explained Yang (Cindy) Yi, an assistant professor in the Bradley Department of Electrical and Computer Engineering.

Yi was awarded the National Science Foundation Faculty Early Career Development Award to design 3-D neuromorphic integrated circuits (IC) that mimic the human brain. 

“My dream for this technology is that it will improve the quality of human life,” said Yi. “We want to build chips to model some of the lost or damaged brain functions, allowing people who have suffered some brain injury to reclaim their former lives or move forward to new ones.”

With the grant, Yi will build on extensive research over the past three years, when she fabricated three IC chips to mimic neural functions. She will be addressing design challenges, including architecture, integration, speed, and efficiency. 

Encompassing evolutionary systems

“The human brain evolved to solve a huge number of complicated problems,” said Yi, “which makes our job easier.”

Based on architecture that mimics bioneurological processes, neuromorphic computing (NC) systems “leverage evolutionary behaviors to address specific problems that have not been solved by current digital computing,” said Yi. 

NC systems are poised to surpass 100 million “neurons” with 1 trillion “synaptic connections” within the near future, Yi noted. They will require high complexity, high connectivity, and massively parallel processing to accomplish increasingly demanding computational tasks.

Traditional integration will be incapable of meeting these requirements, but Yi is exploring this technology in a new dimension—literally. 

3D integrated circuit design 

One of the more obvious advantages the human brain holds over current integrated circuit technologies is that it's not flat. 

Current 2D integrated circuit technology is approaching its physical and material limits, said Yi. She is investigating how 3D integration technology can be used to create a neuromorphic system that is compatible with current technology while operating at high system speed with high density and significant parallel processing, low power consumption, and a small design area.

Computational capacity, scalability, and boosted performance  
By combining the computational capacity of NC networks with the scalability advantages of 3D integration, Yi’s team will be designing NC circuits and systems that more closely emulate the brain’s information-processing infrastructure. 

Yi and her team have already encoded circuits to operate simultaneously at different speeds while carrying complementary information. This technique, called temporal encoding, matches the behavior of brain cells. 

“To the best of our knowledge, the neuron circuit we developed and tested is the first to present sensory data in this way,” said Yi.

To improve reliability and robustness of NC circuits and systems, Yi and her team will be reconfiguring and adapting the high-performance electrical connections that run through silicon so that they can act as membrane capacitors. 

“Membrane capacitors typically occupy a significant portion of a chip’s design area,” explained Yi. By employing inactive electrical connections in a second task, they can substantially reduce design area and boost chip performance.

Emerging applications

“If successful, this technology could fuel potentially disruptive capabilities in real-time data analysis, time-series predictions, environmental perception for autonomous operations, and dynamic control systems,” said Yi. 

It could also improve the performance of current and future systems by significantly decreasing power, size, and weight budgets, and by enabling embedded and retrofit applications on legacy, mobile, and remote platforms. 

Other applications of Yi’s work could improve computing efficiency in wireless communication, cybersecurity, and big-data analysis. 

“Incorporating brain functionality will be an evolutionary change for the field,” said Yi. “It’s a very exciting time to be involved.” 

In addition to the CAREER award, Yi and her students received the 2018 IEEE International Symposium on Quality Electronic Design Best Paper Award and the 2018 IEEE Transmission, Access, and Optical Systems Technical Committee's Best Paper Award for their work on neuromorphic computing. She was awarded the United States Air Force Summer Faculty Fellowship in 2015 and 2016.

Prior to joining Virginia Tech, Yi was conducting research in integrated circuits and systems and high performance computing at the University of Kansas, the University of Missouri-Kansas City, Intel, and Texas Instruments.

She received her bachelor's and master's in electrical engineering from Shanghai Jiao Tong University and her Ph.D. in electrical and computer engineering from Texas A&M University.

Written by Kelly Izlar