Accelerated computing
Physical AI, at the plant edge
Dough is alive and the lines never stop, so perception has to be local and fast. Doughex runs GPU inference at the plant edge and keeps training, simulation and optimisation in the cloud, in a private cloud or on-prem. Items marked planned are on the roadmap, not running.
Jetson Orin / Thor · DeepStream · Holoscan · TensorRT
Edge perception
8 to 24 cameras per line plus inline moisture, weight, thermal and metal or X-ray streams. Design target: sub-100 ms reject decisions, 10 to 60 FPS per station, offline-tolerant operation at the plant edge.
Triton · NIM
Model serving
Bake-prediction, defect, weight, shelf-life and bakery-reasoning models behind one orchestrator, with version routing by product, line and station. Plants run many lines, SKUs and models at once.
DGX / HGX · NeMo · RAPIDS
Training (planned)
Vision, time-series, multimodal and bakery-process models fine-tuned from batch telemetry, images, engineer corrections and lab outcomes. Initial design target: 50 to 200 GPU-hours per product family per release.
Omniverse · Modulus · OVX
Digital twin (planned)
GPU-accelerated oven CFD, proof heat and humidity dynamics, dough rheology and deformation, and robot cells, run as-baked before the run to find quality-safe, low-energy profiles.
Cosmos · Omniverse Replicator
Synthetic data (planned)
Rare foreign-body, under- and over-bake, blistering, cracked-top, hole, deformation and seal or fill scenarios, generated with domain randomisation and validated against real plant events before use.
cuOpt
Optimisation
Oven and changeover scheduling, energy allocation across zones and lines, proof dwell allocation and line balancing, recomputed on the GPU when the line is disrupted.
Isaac · Jetson
Robotic handling
Pan loading and unloading, tray transfers, reject diversion and packaging handling, with perception and motion policies built in Isaac Sim before they touch the line.