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Cornell Unveils First Silicon “Microwave Brain” Chip for Real-Time GHz Computing

Operating at tens of gigahertz with under 200 mW power, the analog microwave processor delivers on-chip signal classification suited for hardware security, scalable integration, edge deployment

Cornell University researchers have come across an exciting discovery.
The chip’s extreme sensitivity to inputs makes it well-suited for hardware security applications like sensing anomalies in wireless communications across multiple bands of microwave frequencies, according to the researchers. Credit: Neuroscience News

Overview

  • The fully integrated silicon chip implements an analog, nonlinear microwave neural network that computes directly in the frequency domain.
  • It processes data streams in the tens of gigahertz while consuming less than 200 milliwatts of power.
  • Benchmark tests showed at least 88% accuracy on wireless signal classification tasks, matching digital neural networks at a fraction of the energy cost.
  • Researchers highlight the chip’s sensitivity for anomaly detection in wireless bands, pointing to potential hardware security uses.
  • Ongoing efforts aim to improve accuracy, cut power consumption further and integrate the technology with conventional microwave and digital platforms for practical edge applications.