HAMILTON, N.Y.Â â Colgate University Professor of Physics Ken Segall is embarking upon collaborative research with Microsoft to explore neuromorphic computing as a low-cost, high-speed alternative to traditional silicon-based computer processors.
Silicon is the semiconductor that forms the basis of todayâs computer circuits, performing calculations like weather forecasts, traffic models, or financial transactions. The electrons that drive these calculations must be powered across the transistors, which sit on silicon chips. Along the way, they bump into surfaces and each other, slowing their speed and causing them to bleed energy in the form of heat.
âBecause there are so many transistors on these chips and they generate heat, theyâre taking more and more power to operate,â Segall said. âAnd if you want to solve big problems, you need a lot of them.â
For example, the Summit supercomputer, currently the worldâs fastest supercomputer, uses more than 73.7 trillion transistors, requiring 4,000 gallons of water per minute for cooling and drawing 13 megawatts â or enough electricity to power nearly 10,000 homes at one time.
âYou can see that this is completely unsustainable,â Segall said.
While the brain has been likened to a computer, Segallâs neuromorphic approach flips the metaphor. It anticipates dynamic, energy-efficient computer hardware that is built like a brain.
Organic brains donât separate memory and processing functions, so humans can identify faces and navigate through space at high speed. Humans learn as we go, prioritizing inputs, growing intellectually stronger where necessary, and letting unimportant information fall away. All of this happens thanks to the rapid transit of electrons along synapses, connecting an estimated 100 billion neurons.
âThere is now a way to think about computing from the point of view of: letâs make something at the hardware level where the connections themselves can change, and we can train a computer to do a different kind of task,â Segall said. âThatâs a very different picture from [the way machines operate] now, where it is all in the software and not in the hardware itself.â
With a startup grant from Colgateâs Picker Interdisciplinary Science Institute, Segall partnered with Associate Professor of Physics Patrick Crotty and Charles G. Hetherington Professor of Mathematics Daniel Schult to model artificial neurons in superconducting metals. They began in 2008 by establishing the mathematics behind the biology, and then, during the following two years, used their data to design a superconducting niobium chip bearing two artificial neurons.
Following experimentation and prototyping, Segallâs team sent electrons through the chip and produced a graph of its firing frequency. It showed behavior similar to that of biological neurons perfectly, dissipating almost zero power. As importantly, the chip rendered its graph in only 15 minutes, compared to the original model, which took two days to complete on a standard computer.
âThere was a biological prediction â this is what the biology says it should do,â Segall said. âWe saw the same behavior in our circuit, at almost 100,000 times faster than the biology.â
Documenting the success achieved in Segallâs lab, he and his colleagues co-published an article in the March 2017 edition of the journal Physical Review. That paper attracted Microsoftâs support for the next phase of exploration. As Professor David Reilly, Microsoft Scientific Director of Microsoft Quantum Lab Sydney, explains, âThere is a prospect for the application of neuromorphic computing to quantum â ultra-low power neuromorphic circuits may be leverageable for controlling quantum devices at scale.â
Microsoftâs investment comes on the heels of a $15 million gift by Trustee Emeritus Robert Hung Ngai Ho â56, Hâ11 to establish the Robert Hung Ngai Ho Mind, Brain, and Behavior Initiative, which will expand the Universityâs interdisciplinary efforts to further understanding around the mind, its functions, and its implications.
âIn these early stages, we are trying to make rudimentary components, to test and see if they are behaving as they should,â Segall said. âOnce we have one, we can scale â thatâs important.â
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