RASEI Welcomes Cheng Wang: Building Smarter, Leaner Hardware for the AI Era

Daniel Morton — Thu, 23 Jul 2026 18:23:13 +0000

RASEI Welcomes Cheng Wang: Building Smarter, Leaner Hardware for the AI Era Daniel Morton Thu, 07/23/2026 - 12:23

Daniel Morton

New Assistant Professor brings expertise in energy-efficient computing to RASEI’s research community

RASEI is pleased to welcome Cheng Wang as an incoming Assistant Professor, who will also be in the Department of Electrical, Computer, and Energy Engineering (ECEE) at 91��ý. Wang is internationally recognized for developing innovative computing hardware that dramatically reduces the energy required for artificial intelligence and other data-intensive applications.

As the increase in AI use drives rapid growth in data centers, much of the conversation has focused on providing electricity and cooling. Wang’s research tackles the challenge from a different direction: making the computers themselves fundamentally more energy efficient through new hardware architectures inspired by the human brain.

Wang joins 91��ý from Iowa State University, where he has spent the last four years building a multidisciplinary research group. Wang brings with him experience that spans both academia and industry, including time as a lead research scientist at Purdue University’s Center for Brain-Inspired Computing and as an R&D Engineer at Seagate Technology. Cheng earned a B.S. in Physics from Peking University and his Ph.D. from the University of Texas at Austin. The work produced by the Wang Group has already received wide recognition, with the funding of an NSF CAREER Award and multiple best paper honors. We’re excited to welcome him, and his research group, into the RASEI community.

Most current computer chips spend as much energy moving data between where the data is stored (the memory) and where the computation is done (the processor) as they do actually processing it. This is a decade-old inefficiency often called the “memory bottleneck”. It is like designing a kitchen where the pantry is on the opposite side of the building to where the stove is. Every time the cook needs an ingredient they must run across the building to grab it. Wang’s research asks what would happen if the chip could compute in the same location as where the data is already stored? The Wang Group designs “in-memory computing”, systems that do exactly that, along with the specialized designs of circuits and architectures that efficiently connect individual computing cores into a scalable system. By removing the need to shuttle data back and forth, significant energy reductions are possible in every single computation.

Another energy-saving approach the Wang Group are exploring begins by asking a more fundamental question: Instead of ‘forcing’ brain-like computing onto current designs of silicon chips, which were never designed for that kind of processing, how much performance and efficiency gains would we get by using hardware that was designed to behave more like a biological brain? The Wang group builds circuits made from nano-electronic and nano-magnetic materials, to behaviorally emulate how biological neurons fire and how synapses strengthen or weaken. Instead of having the typical digital “on/off” states, a more analog “gradient” of states is possible. These approaches have the promise to not simply drive down the energy required for computing, but also open up new approaches and opportunities for alternative modes of computation.

Wang’s research sits at the intersection of computing and energy efficiency, at a time when this is front and center for many communities. As artificial intelligence is being woven into much of today’s business and communication, the electricity demands of the data centers running it are of increasing concern; a core element of RASEI’s mission. Wang’s work on ultra-efficient, brain-inspired hardware offers a path toward a more efficient AI system. We are looking forward to the connections, ideas, and directions the Wang Group will bring to the RASEI research community.

Cheng explains that there were multiple draws that brought his team to the RASEI community. “I am excited to explore collaborations across the full stack of designing new devices and architectures”, said Wang. The expertise in materials science and device design present in the RASEI community, across 91��ý and NLR, are natural partners to the research being done in the Wang group. “I can see many ways in which our research can develop to engage different aspects of the RASEI community” commented Wang. Of particular interest is the opportunity to engage with a new testbed being built at NLR for high-performance computing, specifically focused on optimizing energy efficiency. Cheng and his family are excited to be moving to the mountains, “When possible, I have biked everywhere since the sixth grade, so we are looking forward to exploring the bike paths across the area”.

Seth Marder, Director of RASEI, welcomes Cheng Wang to the community. He says that “Cheng’s research adds a new dimension to RASEI. As AI and data centers grow, much effort focuses on efficient delivery of power and cooling. However, reducing computing energy consumption is equally important. Cheng pioneers innovative approaches to improve computing hardware efficiency. I’m excited to see his impact and believe he’ll help RASEI advance energy-efficient AI and data center technologies”.

Bri-Mathias Hodge, Interim Department Chair of ECEE, highlighted the departments enthusiasm for the addition of Cheng Wang, adding: “We are excited to partner with RASEI to bring another great faculty member to campus. Cheng’s research is an exciting addition to the department, helping us bridge multiple areas of research strength.”

Please join us in welcoming Cheng Wang to RASEI. Take the opportunity to introduce yourself to him and his team.

JULY 2026

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