Electronic Design Flow Studio (EDFS): Deformation-Controlled Operator Inference & Graph-Compiled Numerical Execution
Introducing the world’s first graph-native design-flow operating system: EDFS
Mahdi Haghzadeh, PhD
Cognitave Inc. / MAXDI Inc.
tex@cognitave.com
Abstract
We present Electronic Design Flow Studio (EDFS), a graph-native design and execution environment for deformation-controlled inference systems. EDFS unifies operator-algebraic inference, nonlinear order-parameter dynamics, and deterministic numerical execution into a single workflow spanning visual graph composition, analytical deformation modeling, numerical solvers, and reproducible artifacts.
The framework is motivated by MXD–COGN theory, where inference is treated as a deformation of operator composition rather than a probabilistic update alone. EDFS provides a practical instantiation of this theory through a canvas-based graph language, graph-aware compilation into numerical solvers (Octave/MATLAB), and automatic generation of technical reports. We describe the system architecture, mathematical interpretation, numerical backend, and software lifecycle, and demonstrate how EDFS enables stable, reproducible inference pipelines suitable for RF systems, nonlinear signal processing, and neuromorphic analog computation.
Introduction
Modern inference systems increasingly rely on nonlinear, context-sensitive dynamics that exceed the expressive power of static probabilistic graphs or feedforward pipelines. In RF systems, neuromorphic hardware, and adaptive sensing, inference is shaped by deformation—changes in operator structure, coupling, and stability rather than by probability updates alone. EDFS was developed to address this gap by providing:
• A graph-native design language for inference pipelines
• A deformation-controlled execution model grounded in operator algebra
• A deterministic numerical backend aligned with physical systems
• Reproducible artifacts (LaTeX reports, numerical solvers)
The system is designed to bridge theoretical inference models and practical electronic and computational implementations.
EDFS Architecture
Graph Layer
EDFS uses a directed graph abstraction:
• Nodes represent inference operators
• Edges represent data/state propagation
• Graph topology defines execution order
This graph is the single source of truth for visualization, execution, and code generation.
Runtime Engine
The runtime engine normalizes the graph into a canonical execution DAG and evaluates deformation controlled dynamics using stable numerical methods. Outputs include:
• State trajectories
• Derived observables (e.g., Smith trajectories, MXD disk evolution)
• Stability and margin metrics (NxS)
Numerical Backend
EDFS compiles graphs into deterministic numerical solvers using:
• Explicit ODE integration (RK4)
• Deformation-aware state updates
• Operator libraries mapping graph nodes to executable functions
Generated solvers are exported as Octave/MATLAB code, ensuring reproducibility and au-
ditability.
Artifact Generation
EDFS automatically generates:
• LaTeX technical reports summarizing scenarios, parameters, and results
• Executable numerical code derived from the graph
These artifacts allow program managers, researchers, and engineers to review, validate, and
reproduce results without interacting directly with the UI.
Implementation and Lifecycle
EDFS development follows a staged lifecycle:
1. HTTP-based developer mode (Phase 1)
2. Native application shell (Phase 2)
3. Hardware and system integration (Phase 3)
Phase 1 culminated in a beta-ready HTTP package with a formal interface contract, deterministic outputs, and frozen viewer assets.
Applications
EDFS is applicable to:
• RF chain analysis and nonlinear stability evaluation
• Neuromorphic and analog inference hardware
• Adaptive sensing and control systems
• Computational inference under nonstationary conditions
The deformation-controlled approach naturally aligns with systems where inference and dynamics are inseparable.
Discussion
EDFS demonstrates that inference systems can be designed as executable operator geometries. By treating deformation as a first-class concept, the framework enables controlled nonlinearity, stability-aware inference, and faithful numerical realization. The separation between graph design and numerical execution further enables cross-backend portability and future hardware mapping (e.g., analog circuits, RF subsystems).
Conclusion
We introduced EDFS, a graph-native environment for deformation-controlled inference. By unifying operator algebra, numerical solvers, and reproducible artifacts, EDFS provides a practical foundation for next-generation inference systems at the intersection of physics, computation, and electronics.
