| | --- |
| | license: other |
| | title: RFT Adaptive Computing Kernel |
| | sdk: gradio |
| | emoji: 🚀 |
| | colorFrom: blue |
| | colorTo: green |
| | short_description: Adaptive RFT kernel computing stability and coherence metric |
| | sdk_version: 6.0.0 |
| | thumbnail: >- |
| | https://cdn-uploads.huggingface.co/production/uploads/685edcb04796127b024b4805/2T1X6xZm2w-L3hdCwtFbM.png |
| | --- |
| | # 🚀 RFT Adaptive Computing Kernel (v1.0) |
| |
|
| | The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** demonstrates real-time compute stability and harmonic coherence across CPU, GPU, and TPU workloads. |
| | It applies RFT’s motion-based harmonic model to show how computation can self-balance under noise, load, or timing variance. |
| |
|
| | --- |
| |
|
| | ## 🔧 Overview |
| | This kernel simulates adaptive performance regulation through harmonic metrics: |
| |
|
| | | Metric | Description | |
| | |---------|-------------| |
| | | **QΩ** | Harmonic stability (amplitude equilibrium). | |
| | | **ζ_sync** | Synchronisation coherence (phase alignment). | |
| | | **items/sec** | Throughput estimate after adaptive correction. | |
| | | **status** | System state — nominal / perturbed / critical. | |
| | |
| | --- |
| | |
| | ## 🧩 Profiles |
| | - **CPU** — Linear compute flow tests. |
| | - **GPU** — Parallel matrix or transformer operations. |
| | - **TPU** — Tensor inference and batch stability. |
| | - **Mixed / I/O** — Combined memory and data-path stress tests. |
| | |
| | --- |
| | |
| | ## ⚙️ How to Use |
| | 1. Choose a **Profile** and **Workload**. |
| | 2. Adjust **Noise σ** (0 – 0.30) to simulate load variation. |
| | 3. Run the kernel. |
| | 4. Review the JSON output showing QΩ, ζ_sync, items/sec, and stability status. |
| | 5. Optionally download the run log for SHA-512 verification. |
| | |
| | Repeated runs at fixed σ demonstrate adaptive recovery and equilibrium maintenance. |
| | |
| | --- |
| | |
| | ## 🎯 Purpose |
| | The Adaptive Computing Kernel bridges theoretical physics and computer engineering by proving that RFT’s harmonic feedback can stabilise computation itself—creating a self-governing, energy-efficient framework for AI, aerospace, and energy systems. |
| | |
| | --- |
| | |
| | ## ⚖️ Rights & Contact |
| | All Rights Reserved — **RFT-IPURL v1.0 (UK / Berne Convention)** |
| | Research validation use only; no reverse-engineering or redistribution without written consent. |
| |
|
| | **Author:** Liam Grinstead |
| | **Affiliation:** Rendered Frame Theory Systems (RFTSystems) |
| | **DOI:** [https://doi.org/10.5281/zenodo.17466722](https://doi.org/10.5281/zenodo.17466722) |
