First IEEE Conference on Rebooting Computing Focuses on Neuromorphic and Quantum Designs

Researchers discuss how to make machines more like the human brain—and faster and more energy-efficient

28 October 2016

Researchers in every field of computing are working on how to advance the next generation of computers. More than 150 participants convened at the first IEEE International Conference on Rebooting Computing, 17 to 19 October in San Diego, where they shared their research and learned from one another’s work.

Hosted by the IEEE Future Directions Committee, the organization’s R&D arm, the event focused on several areas of computing, including neuromorphic, which aims to mimic the brain; optical and quantum; and fabrication. IEEE Member Robert Leland, CTO at Sandia National Laboratories, in Albuquerque, kicked off the conference with a presentation on how national security concerns over the years, such as the threat of war, have pushed high-performance computing forward. The ENIAC computer, which was designed during World War II to help the U.S. Army calculate potential enemy attacks, is one example.

The message throughout the conference was that all those working in the field, regardless of industry or nation, must come together to reach the next level of computing. Here are some of the highlights.


Neuromorphic computing is designed to mimic the brain to improve computer processing so machines can learn quickly. It is such a hot topic that 17 sessions were devoted to it. Speakers addressed the need for adaptive neural learning, in which machines learn as they have new experiences, as well as “brain chips” that can process information from data as soon as it is generated.

IEEE Senior Member David Mountain, technical director at the U.S. Department of Defense, spoke about neuromorphic computing’s technological considerations, including the need for new architectures. He explained how design choices could help advance neural networks.

A session on brain-inspired computing systems led by IEEE Senior Member Luping Shi, a professor at Tsinghua University, in Beijing, addressed how neuromorphic computing can help overcome grand challenges including moving beyond Moore’s Law. Shi also discussed what insights can be made from the brain to inspire future computing systems.


IEEE Senior Member Joe Touch, director of the Information Sciences Institute at the University of Southern California, in Los Angeles, gave a talk on optical computing, which relies on photons produced by lasers or diodes for computation. Photons could provide higher bandwidth than the electrons currently used in conventional computers and could improve the machines’ energy efficiency. Touch led “The Optical Turing Machine,” a session that considered a new approach to support in-transit network packet processing, big data filtering, and security.

Quantum computing focuses on theoretical computation systems that promise performance exponentially faster than any of today’s computers. IEEE Senior Member Alan M. Kadin, however, in his talk on “Proposed Experiments to Test the Foundations of Quantum Computing,” suggested there might be serious problems with quantum computing, because experiments to date have not proven that the machines will be as fast as what researchers claim. An adjunct professor of electrical engineering at the College of New Jersey, in Ewing, Kadin recommended new experiments to test whether quantum computing can achieve promised performance.


In his presentation, IEEE Member Neil Gershenfeld said, “the killer app for computation isn’t faster computers, but fabrication.” In other words: machines that make machines. Gershenfeld is founder and director of MIT’s Center for Bits and Atoms, an initiative focused on exploring the relationship between computer and physical sciences.

He said he relies on biology to learn how to create self-producing machines with nonbiological materials. Tools such as 3-D printers can create larger structures such as parts for airplanes and spaceships, while electron microscopes and ion beam probes can be used for nanostructures. The next step in fabrication is not cutting or printing, he said, but putting code into the materials—similar to how our genomes are coded. He calls it digital fabrication. With the right code, he said, materials will be able to replicate without much help from people.

Gershenfeld says it’s time for a do-over in computing, and the new way to compute might look a lot like the natural world, with a focus on machines that can adapt to the environment they are in.

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