Edge compute for vision and sensor inference at the machine, so that decisions with a hard latency budget do not wait on a network round trip and video does not have to leave the site. Both of those constraints are usually non-negotiable, which is what makes this hardware rather than a cloud service.
A reject actuation has to fire before the part leaves the station. An interlock has to trip before someone reaches the machine. Neither can depend on a network round trip, which rules out cloud inference regardless of how much compute the cloud has.
The second constraint is data volume and privacy. Streaming continuous video from twenty cameras is expensive in bandwidth, fragile when the link degrades, and awkward under most data-protection regimes. Running inference locally means only events and metadata leave the site — and in many configurations, no imagery leaves at all.
Sizing the node is the engineering. Worst-case latency, not average, has to fit inside the cycle time the line allows, and that is measured on the actual hardware rather than estimated from a model's operation count.
| Parameter | Detail |
|---|---|
| Function | Local inference for vision and sensor workloads |
| Latency | Bounded and measured at worst case, not average |
| Data | Events and metadata leave the site; imagery need not |
| Sizing | Against the cycle time and model, measured on target hardware |
| Deployment | At the machine, cell or site depending on stream count |
| Typical markets | Industrial AI, machine vision, site safety, mobility |
Maturity — silicon-proven, FPGA-validated or RTL stage — is confirmed at enquiry for the specific configuration you need, rather than claimed generically here.
Send the target node, the interface requirements and the integration context. If this is not the right fit, that will be said early rather than discovered at integration.
Because decisions with a hard latency budget cannot depend on a network round trip, because streaming continuous video is expensive and fragile, and because in many deployments imagery must not leave the site at all.
Against the worst-case latency the cycle time allows, measured on the actual hardware. Framework-reported operation counts correlate poorly with wall-clock latency on embedded targets, where memory bandwidth usually dominates.
Not necessarily. Systems can be configured to process frames locally and emit only events, counts and metadata, retaining no imagery.
Tell us the specification, the constraint and the deadline. Programmes that cross silicon, radio, embedded and AI are where Faststream is strongest.