Graiphic GO Whitepaper Series

A curated collection of Graiphic’s technical whitepapers describing a unified approach to graph computing, AI orchestration, informed machine learning, and hardware–software unification.

These documents form a coherent program built on a shared foundation:
ONNX as a universal graph format, ONNX Runtime as the execution engine, and SOTA as the graphical orchestration environment.

Each whitepaper resides in its own folder with the corresponding PDF and associated visual material.

📄 Whitepapers & Repositories

1. SOTA — The LabVIEW IDE for Graph Computing (v1.0)

Theme: ONNX-native authoring, compilation, and orchestration inside LabVIEW
Summary:
SOTA is the first building block of Graiphic’s technology stack and a fully functional platform already available to industry, research laboratories, and academic institutions.
It provides a unified graphical environment where engineers can author, edit, train, optimize, and deploy ONNX graphs visually inside LabVIEW.
SOTA defines the common execution model, tooling, and orchestration principles used across all GO initiatives.

Whitepaper:
👉 SOTA GO-Whitepaper_1.0.pdf
Folder:
👉 SOTA GO

2. GO HW — From Models to Systems (v1.4)

Theme: Hardware orchestration through ONNX
Summary:
GO HW extends ONNX beyond AI inference into a deterministic system and hardware orchestration graph.
It introduces hardware primitives such as GPIO, DMA, ADC/DAC, timers, and energy-aware execution as first-class graph nodes, enabling real-time deployment across CPUs, GPUs, FPGAs, NPUs, and embedded SoCs using a single ONNX artifact.

Whitepaper:
👉 GO-HW_Whitepaper_1.4.pdf
Folder:
👉 GO HW — From Models to Systems

3. GO GenAI — From Fragmentation to Orchestration (v1.1)

Theme: Unified orchestration of Generative AI through ONNX
Summary:
GO GenAI addresses the fragmentation of the Generative AI ecosystem by transforming ONNX into a dynamic orchestration fabric.
It enables deterministic coordination of models, tokenizers, data streams, logic, and heterogeneous hardware runtimes inside a single executable graph, fully integrated within SOTA and without reliance on Python glue code.

Whitepaper:
👉 GO-GenAI_Whitepaper_1.1.pdf
Folder:
👉 GO GenAI — From Fragmentation to Orchestration

4. GO IML — From Theory to Superiority (v1.1)

Theme: Informed Machine Learning inside the ONNX graph
Summary:
GO IML introduces Informed Machine Learning as a native graph-based capability.
It embeds physics, logic, constraints, and expert knowledge directly into ONNX training graphs, enabling faster convergence, stronger generalization, explainability, and robust deployment on real-world hardware.

Whitepaper:
👉 GO IML_Whitepaper_1.1.pdf
Folder:
👉 GO IML — From Theory to Superiority

🔗 Learn More

🌐 Website: https://www.graiphic.io
📺 YouTube: https://www.youtube.com/@graiphic
📰 News & Insights: https://graiphic.io/news-insights/

✉️ Contact

📧 hello@graiphic.io

Graiphic/GO-Whitepapers