VectorWave offers a neuromorphic analog computing platform that performs AI inference directly on raw radio-frequency signals before digitization, delivering nano‑ to pico‑second latency and eliminating the memory‑compute round‑trip. This enables ultra‑low‑latency, power‑efficient decision‑making at the RF edge for dynamic spectrum sharing, jam‑resistant reception, and real‑time spectrum management, targeting telecom equipment makers, network operators, and defense/aerospace users.
Funding
Funding not disclosed
Founders
Product
Problem
Current edge AI solutions for radio-frequency (RF) systems rely on digitizing raw waveforms and then moving data between memory and compute units, introducing millisecond‑scale latency and high power consumption. This architecture limits real‑time spectrum management, jam resistance, and rapid decision‑making in congested or contested RF environments.
Solution
VectorWave provides a neuromorphic analog computing platform that performs AI inference directly on raw RF signals before digitization. By eliminating the memory‑compute round‑trip, the system delivers nano‑ to pico‑second latency, enabling instantaneous awareness and control at the point where the signal is received. This edge‑native intelligence removes the need for power‑hungry digital AI hardware and cloud round‑trips, allowing devices to dynamically share spectrum, prioritize traffic, and maintain performance even in heavily interfered or jammed conditions. The approach creates a new computational foundation for real‑time RF decision‑making across the entire spectrum.
Target Audience
Primary customers are telecommunications equipment manufacturers, network operators, and defense or aerospace organizations that require ultra‑low‑latency, resilient RF processing for base stations, mobile devices, IoT sensors, and mission‑critical receivers.
Features
- Neuromorphic analog architecture that processes raw radio waveforms without prior digitization
- Inference latency in the nano‑ to pico‑second range, orders of magnitude faster than conventional millisecond‑scale systems
- Direct RF‑edge computation removes the memory‑compute data transfer penalty, reducing power consumption
- Real‑time dynamic spectrum coexistence, enabling devices to sense and prioritize frequencies on the fly
- Jam‑resistant and interference‑tolerant operation by making decisions at the waveform level
- Compatibility with existing RF front‑ends, allowing integration into base stations, IoT sensors, and other communication hardware