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
License: RFT-IPURL v1.0 — Research validation use only.