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