Broadband encoding and high-speed probabilistic bit generation with integrated microwave neurons

Intelligence at high data rates depends on extracting meaning as information arrives. Although radio signals are analog, most systems digitize them before feature extraction, adding latency and power overhead while limiting processing speed to digital clock rates. Instead, we find that incoming data can itself reprogram coupling between frequency modes in an ensemble of microwave waveguides, encoding spectral features across several gigahertz. This milliwatt-scale Microwave Neural Network (MNN), manufactured in Complementary-Metal-Oxide-Semiconductor (CMOS) technology, expresses data tokens across a spread spectrum, making relationships between successive tokens easier to detect and enabling faster downstream decisions with lightweight digital processing. We also find that gigabit-per-second bitstreams, like 8-bit pixel data, drive the MNN to produce probabilistic bits whose bias reflects the input pattern. This analog dithering preserves detail in compressed data, potentially enabling richer satellite imaging at one-eighth the bit-rate under bandwidth constraints. A neural network of coupled microwave waveguides encodes features of analog data across a broad frequency band to make edge decision-making easier. It can also be driven by gigabit streams to produce probabilistic bits that enable data compression.