Cornell Creates ‘Microwave Brain’ Chip for Ultrafast Computing
New York: Cornell University engineers have introduced the world’s first processor built as a “microwave brain.” The experimental microchip can compute ultrafast data and wireless communication signals by applying the physics of microwaves. Because it consumes less than 200 milliwatts of power, it sets a new benchmark in energy-efficient processing.
The research, published in Nature Electronics, reveals the first fully integrated microwave neural network on silicon. Unlike traditional processors, the chip performs real-time frequency domain computation. As a result, it can handle tasks like radio signal decoding, radar tracking, and digital data analysis with unmatched efficiency.
Its innovative design functions as a neural network with tunable waveguides that recognize patterns and learn from input. While digital processors rely on step-by-step logic, this chip uses analog and nonlinear microwave behaviors. Consequently, it processes streams in the tens of gigahertz, well beyond the reach of most digital systems.
Researcher Insights and Testing Results
Lead author Bal Govind, a doctoral student, remarked that, “Because it’s able to distort in a programmable way across a wide band of frequencies, it can be repurposed for several computing tasks. It bypasses a large number of signal processing steps that digital computers normally have to do.”
Co-senior author Alyssa Apsel, professor of engineering, explained the unique design approach. She said, “Instead of trying to mimic digital neural networks exactly, he created something that looks more like a controlled mush of frequency behaviors that ultimately gives you high-performance computation.”
Laboratory tests demonstrated the chip’s ability to execute both simple logic operations and complex computations. It successfully identified bit sequences and counted binary values in high-speed data streams. Moreover, it reached 88 percent or higher accuracy in several wireless signal classification tasks. These results matched digital neural networks but required far less power and chip space.
Potential Applications and Future Development
The processor’s high sensitivity makes it especially attractive for security purposes, including anomaly detection in wireless communications across microwave bands. Because of its energy efficiency, it also holds promise for edge computing. With further refinements, researchers envision embedding models directly into smartphones or wearables, reducing dependence on cloud processing.
Funded by the Defense Advanced Research Projects Agency and developed at the Cornell NanoScale Science and Technology Facility, the project is still in the experimental stage. However, its scalability and integration potential position it as a transformative step toward next-generation computing systems.
