Pipeline as product
The project has already evolved past ad hoc scripts. There is a control surface, stored run metadata, artifact contracts, and a named stage graph that explains how a run moves forward.
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Execution
Program spine
Model To Mask Flow
NeuroEtch is structured as a pipeline program, not a loose collection of experiments. The webapp, control API, runtime API, and workers are all there to make the translation chain observable.
Key points
The flow is valuable because it ties abstract architecture decisions to actual produced artifacts.
The repo already has a model of execution modes and evidence quality; the public site should expose that maturity.
An honest hardware program needs this pipeline even before the final silicon is ready.
Major stages
Model -> RTL -> EDA -> GDS
The whole stack is represented in the platform.
Worker mode
Evidence-gated
Synthetic and physical modes are separated.
Operational surface
Control + Runtime APIs
The platform keeps orchestration explicit.
Why the flow matters
Many chip ideas die in the gap between architectural rhetoric and repeatable implementation. NeuroEtch narrows that gap by making each transition legible and testable.
Model artifacts are named and typed.
RTL generation and EDA adapters are explicit stages.
Output evidence is tied back to runs and reports.