Unified graph computing for AI, logic, hardware, and deployable systems.
This library gathers Graiphic's GO whitepapers into a single GitHub Pages experience built around ONNX as a graph format, ONNX Runtime as an execution layer, and SOTA as the visual orchestration environment.
Each whitepaper lives in its own folder with:
- a Markdown presentation page
- the original PDF
- supporting illustrations and diagrams
Use the sidebar to jump directly between documents or open the PDF versions from the download section.
Theme: ONNX-native authoring, compilation, and orchestration inside LabVIEW.
The foundational visual environment for building, training, deploying, and orchestrating ONNX graphs across the Graiphic stack.
Theme: Hardware orchestration through ONNX.
Extends the graph model beyond inference into deterministic orchestration of I/O, DMA, timing, and heterogeneous hardware targets.
Theme: Unified orchestration of Generative AI through ONNX.
Transforms fragmented GenAI stacks into a single graph-based system that combines models, tokenizers, logic, memory, and runtime orchestration.
SOTA GO is the operational foundation of Graiphic's technology stack. It provides a unified visual cockpit where engineers can author, inspect, train, optimize, deploy, and operate ONNX graphs directly inside LabVIEW.
GO HW extends ONNX beyond model execution into deterministic system orchestration. It introduces graph-level hardware primitives for GPIO, DMA, ADC, DAC, timers, synchronization, and energy-aware deployment.
GO GenAI reframes generative AI as a system orchestration problem. Models, tokenizers, control flow, memory, preprocessing, postprocessing, and hardware execution become part of one executable graph.
GO IML introduces Informed Machine Learning as a graph-native capability where priors, symbolic logic, physical constraints, and expert knowledge directly shape the learning process and final deployed system.
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