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Edge ML (Planned)

The Consystence edge runtime today is a .NET 10 single-silo Orleans application focused on deterministic PLC communication, store-and-forward buffering and upstream streaming — it does not run machine-learning inference. The longer-term plan is to add a constrained, advisory-only inference capability at the edge so that anomaly and condition hints can be surfaced even when the edge’s upstream link is down.

Design intent

Section titled “Design intent”

The planned model is deliberately narrow:

  • Cloud-trained, edge-served. Models are trained centrally (in the cloud, where the data and compute live) and exported to ONNX. The edge never trains; it only runs inference against a frozen exported model.
  • CPU baseline, GPU where the workload needs it. Baseline inference runs on the edge CPU, so it works on any edge host, including the PLCnext VL3 target platform — GPU is never assumed. GPU-compiled inference (TensorRT) on an Nvidia Orin is the design option where a site’s workload demands it, such as vision models or complex autoencoders. The edge runtime is the same on either host, and an Orin is not a standalone ML appliance. See Orin setup and VL3 edge setup.
  • Strictly advisory. Inference output is limited to hints — anomaly scores, condition/health classifications, early-failure indicators — surfaced to operators for acknowledgement. It is one more advisory signal alongside the advisory assistant, never a control input.

Advisory-only is non-negotiable

Section titled “Advisory-only is non-negotiable”

This constraint exists because industrial safety standards and mining regulation require a deterministic, auditable control path, and ML models are probabilistic and opaque. The same boundary that applies to the advisory assistant applies, unchanged, to edge inference: edge ML produces hints, an operator decides, and the controller acts. There is no closed-loop, model-in-the-loop control path — planned or otherwise.

flowchart LR
    subgraph cloud[Cloud / cell]
        T[Train model<br/>export to ONNX]
    end
    subgraph edge[Edge runtime - planned]
        I[CPU inference baseline<br/>TensorRT GPU on Orin]
        H[Anomaly / condition<br/>hints]
    end
    O[Operator<br/>acknowledges]
    C[Controller<br/>deterministic path]
    T -->|signed model artefact| I
    I --> H
    H -->|advisory only| O
    O -->|executes| C

Explicitly out of scope

Section titled “Explicitly out of scope”

To avoid overclaiming, the following are not part of the platform today:

  • No TensorRT, .engine artefacts, or other GPU-compiled inference engines ship on edge nodes today — TensorRT on Orin is part of the documented design for GPU-dependent workloads, but nothing is deployable.
  • No GPU-required inference on the baseline path — the planned baseline is CPU ONNX, so it would run on an edge host without a GPU; GPU-dependent workloads would use an Orin’s GPU.
  • No fleet-wide hot-loading or live model push to running edge nodes.
  • No on-device training, fine-tuning, or feedback learning at the edge.

When edge ML does arrive, model artefacts are expected to follow the same account-signed distribution discipline used elsewhere on the platform, and to degrade rather than block — if a model is absent, stale, or fails to load, the edge continues its deterministic PLC and store-and-forward work unaffected.

Section titled “Related reading”