AI inference moves to the acquisition front end

The system performs its AI inference directly within the digitizer, rather than transferring raw data to a host computer for offline processing. This architecture allows the 10-GSPS sampling rate to be combined with immediate decision-making, enabling overlap between particle signals to be identified as the data streams in.

Throughput and latency figures

The reported performance characteristics are:

• Sampling rate: 10 GSPS
• AI inference capability: ultra-high-speed, integrated into the acquisition chain
• Latency: 300 ns from signal to detection result
• Sustained data processing: 120 Gbit/s

Such latency figures are in the range required for trigger-level decisions in high-rate experimental environments, where events must be classified in real time before data can be filtered or stored.

Relevance for physics and instrumentation

Real-time overlap detection addresses a core challenge in particle instrumentation, where signals arriving in quick succession can otherwise be indistinguishable without computationally intensive post-processing. Embedding inference at the point of sampling reduces the volume of data that must be transferred and stored downstream.

The claim, as published, checks out as True.