Rome Lab awards SUNY Poly professor nearly $1.8M grant

Air Force Research Laboratory (AFRL) in Rome has awarded Nate Cady, a nanobioscience professor at SUNY Polytechnic Institute in Albany, nearly $1.8M in funding for research in developing next-generation computer systems. (PHOTO CREDIT: SUNY Polytechnic Institute)

ROME — Air Force Research Laboratory (AFRL) in Rome, known as Rome Lab, has awarded a SUNY Polytechnic Institute (SUNY Poly) professor nearly $1.8 million in funding for work in developing next-generation computer systems. Nate Cady, a nanobioscience professor at SUNY Poly’s Albany campus, will use the funding to enable future generations of computing systems […]

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ROME — Air Force Research Laboratory (AFRL) in Rome, known as Rome Lab, has awarded a SUNY Polytechnic Institute (SUNY Poly) professor nearly $1.8 million in funding for work in developing next-generation computer systems.

Nate Cady, a nanobioscience professor at SUNY Poly’s Albany campus, will use the funding to enable future generations of computing systems by using memristors (or “memory resistors”). Memristors are nanoscale electronic switching devices that act like synapses in the human brain.

The funding award will allow Cady and his research team to create an overall hardware architecture and capability which can result in computing that can be “as much as 1,000 times as powerful as is currently available,” per an April 3 SUNY Poly news release. 

Supporting educational opportunities for a number of SUNY Poly students, this research will leverage the institution’s 300mm and 200mm fab facilities and research labs, in order to provide neuromorphic computing power that meets “stringent” Air Force requirements for applications such as unmanned aerial vehicles (UAVs), aircraft, satellites, and other deployable autonomous systems.

“On behalf of SUNY Poly, I am thrilled to congratulate Professor Cady on this latest grant from the Air Force Research Laboratory. It showcases the high-impact research our faculty conducts, as well as our world-class fabrication capabilities that are advancing next-generation computing while addressing power-consumption challenges to enable autonomous and deployable systems that can enhance our nation’s security and improve a number of the technologies we use each day,” Grace Wang, interim president of SUNY Poly, said. “This significant AFRL award is the most recent testament to SUNY Poly’s meaningful research, providing exciting opportunities for students to learn how these devices are fabricated.”

The Air Force Research Lab Information Directorate said it looks forward to working together with SUNY Poly in research and development of “hybrid CMOS/memristor processes that will enable powerful neuromorphic and other architectures” with greater capabilities for Air Force systems. 

“The advanced manufacturing capabilities at SUNY Poly further allow for rapid prototype development and fielding to the warfighter, which is critical for the Air Force rapid acquisition system,” Joseph Van Nostrand, principal electronics engineer, AFRL/RITB, and program manager, said in the SUNY Poly release. 

This follows an announcement last fall that the National Science Foundation awarded Cady $500,000 in funding from to develop advanced computing systems based on a “novel approach” to the creation of non-volatile memory architecture.

The research

Cady’s research, titled “Fabrication of Efficient Reconfigurable Neuromorphic Systems,” seeks to address the “significant slowdown” that has taken place in the expected performance improvements that result from scaling computer chips to smaller and smaller sizes.

It also looks to improve power consumption, which can “often be a critical limiting factor” for device performance. 

Cady’s research will focus on avoiding the “von Neumann bottleneck,” which currently results from the separate location of the processor and memory. This separation creates a limit on data throughput. However, combining storage and computation on the same device can avoid the bottleneck, SUNY Poly said. 

The research focuses on integrating logic and memory to achieve so-called “compute in memory” operations, “which are similar to how the human brain functions.” These neuro-inspired computer architectures can also perform computing tasks by breaking down information into low-voltage “spikes,” which saves power and enables the chips to “learn on the fly.” 

As a result of this increased computing capacity, these neuro-inspired computer architectures may be “particularly well suited” to handle problems requiring techniques and systems that can capture knowledge from an abundance of data, SUNY Poly said. 

For example, they could be “highly relevant” for advancing the internet of things, as well as a number of deployable, autonomous systems by capitalizing not only on its computing power, but also by seeking approaches that lead to “low-power, reconfigurable, high-efficiency brain-inspired computing capabilities.” 

Cady’s research will support a SUNY Poly postdoctoral researcher and graduate students, as well as a number of undergraduate students, who will be able to “learn first-hand” how to develop and fabricate the memristive neuromorphic structures, the university said.

“This grant is a perfect example of how our faculty’s cutting-edge research can help to tackle challenges such as computing bottlenecks and address them through the use of innovative solutions, which are possible through the use of SUNY Poly’s advanced facilities and resources,” Steven Schneider, interim provost at SUNY Poly, said in the release. “This announcement is also impactful for a number of our students who will be able to gain the unique opportunity to work on these devices and obtain unmatched lab and fab experience.”

“I am grateful to the AFRL for supporting our research which now represents a successful transition from the first memristive devices that were developed at SUNY Poly using the state-of-the-art resources available here, to developing an actual product — a functional neuromorphic computer chip,” Cady. “A key challenge that our research seeks to overcome is how to improve memristor performance from the standpoint of reliability and power consumption. I look forward to working on the full integration of our memristive devices into a full processor to implement low-power neuromorphic computation that is also capable of high accuracy.”  

Eric Reinhardt: