TECHNICAL_NOTES.md

# 🚀 RFT Adaptive Computing Kernel — Technical Notes (v1.0)

The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** benchmarks computational self-stabilization, throughput efficiency, and coherence across **CPU, GPU, and TPU** workloads.

It integrates RFT’s harmonic feedback system (QΩ / ζ_sync) with adaptive governors to regulate:
- **Clock scaling**
- **Thermal headroom**
- **Duty-cycle performance**
- **Noise-induced computation drift**

This system demonstrates how RFT’s harmonic framework can stabilize compute throughput and coherence across different hardware and noise environments.

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## ⚡ Core Function
Each run simulates workloads and injects synthetic noise or load imbalance.  
RFT’s adaptive controller adjusts clock scale and workload timing in real time to preserve frame-level stability.  
It outputs:

| Metric | Description |
|---------|--------------|
| **QΩ** | Harmonic stability of compute cycles (amplitude equilibrium). |
| **ζ_sync** | Synchronization coherence between threads/cores. |
| **items/sec** | Estimated throughput efficiency after stability correction. |
| **status** | System condition: nominal / perturbed / critical. |

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## 🧩 Supported Profiles
| Profile | Description |
|----------|--------------|
| **CPU** | Scalar or integer workloads — stability under linear compute load. |
| **GPU** | Parallel workloads (matrix or transform) — coherence under high variance. |
| **TPU** | Tensor workloads — synchronization in large-batch inference. |
| **Mixed / I/O** | Combines memory, disk, and network delay tests for system-level drift study. |

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## ⚙️ Internal Dynamics
- **Adaptive Governor:** Modulates internal load scaling (clock, thread, or matrix block size).  
- **Noise Control:** Applies synthetic perturbation (σ = 0.00–0.30) to simulate real-world variance.  
- **Micro-Benchmark:** Runs lightweight compute cycles and reports items/sec safely.  
- **Feedback Loop:** Uses QΩ/ζ_sync variance as control input for next iteration (adaptive self-correction).  
- **Bounded Validation:** Metrics capped to realistic operational limits.

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## 📈 How to Use
1. Choose **Profile** → CPU / GPU / TPU / Mixed.  
2. Adjust **Noise Level (σ)** and sample count.  
3. Run simulation.  
4. Observe:
   - **items/sec** → throughput stability  
   - **QΩ / ζ_sync** → harmonic state  
   - **status** → equilibrium condition

Repeated runs at identical σ show adaptive stability improvement.

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## 🧮 Interpretation
| Status | Description | Expected Behavior |
|---------|--------------|-------------------|
| **Nominal** | Stable equilibrium | Items/sec consistent, QΩ ≈ ζ_sync |
| **Perturbed** | Transitional adjustment | Minor drops in throughput, recovery visible |
| **Critical** | Overload or divergence | Severe drop or incoherence detected |

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## 🔐 Verification & Rights
All adaptive logic and governing equations are protected under **RFT-IPURL v1.0** and the **Berne Convention (UK Copyright Law)**.  
All performance runs are timestamped and may be SHA-512 sealed for traceable verification.

**Author:** Liam Grinstead  
**Affiliation:** Rendered Frame Theory Systems (RFTSystems)  
**DOI:** [https://doi.org/10.5281/zenodo.17466722](https://doi.org/10.5281/zenodo.17466722)  
**License:** RFT-IPURL v1.0 — Research validation use only.