BANTAI Photonic Compute Stack
A cutting-edge Deep Learning framework simulating Diamond-Glass Hybrid Photonic Chips.
BANTAI Kecia is a lightweight, high-performance neural network SDK that simulates mathematical interference of light waves for deep learning computations (Optical MAC Unit) and uses a custom 5D Glass Memory system (.kecia format) for zero-data-loss weight storage.
✨ Key Features
- Optical MAC Simulation: Performs Vector-Matrix Multiplication (VMM) at simulated speed-of-light using light intensity and phase.
- 5D Glass Memory System: Securely saves and loads trained models using the proprietary
.keciaformat. - Hardware Fallback Logic: 3-Tier automatic execution fallback (Photonic -> CUDA -> CPU).
- Clean PyTorch-like API: User-friendly and production-ready interface.
- Built-in Autograd: Supports custom Backpropagation and SGD Optimizer.
📦 Installation
You can easily install the BANTAI SDK via pip:
pip install bantai-kecia
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Release files for bantai-kecia 1.0.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
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| bantai_kecia-1.0.0.tar.gz | 3.6 kB | Without provenance | Details for bantai_kecia-1.0.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
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| bantai_kecia-1.0.0-py3-none-any.whl | Python 3 | none | any | Details for bantai_kecia-1.0.0-py3-none-any.whl |
Total release size: 5.6 kB
Release files / bantai_kecia-1.0.0.tar.gz
| Download URL | bantai_kecia-1.0.0.tar.gz |
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| Size | 3.6 kB |
| Tags | Source |
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| Download URL | bantai_kecia-1.0.0-py3-none-any.whl |
|---|---|
| Size | 1.9 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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