|
Download README.md from RFTSystems/RFT_Adaptive_Computing_Kernel: direct link, hf CLI and curl.
- Browser
- Download file 2.37 kB
-
https://huggingface.co/spaces/RFTSystems/RFT_Adaptive_Computing_Kernel/resolve/main/README.md
- Command line
-
hf download hf://spaces/RFTSystems/RFT_Adaptive_Computing_Kernel/README.md
-
curl -L -o README.md https://huggingface.co/spaces/RFTSystems/RFT_Adaptive_Computing_Kernel/resolve/main/README.md
2.37 kB
| 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) |