---
license: other
license_name: other
license_link: LICENSE
---
---
tags:
- green-ai
- edge-computing
- c++
- spectral-graph-theory
- ramanujan-graphs
- topological-deep-learning
license: other
---

# ⚡ Ramanujan Spectral Reservoir (RSR)

> **"Intelligence is not about weight adjustment, but optimal topology."**

This repository hosts the reference implementation and benchmarks for the **Ramanujan Spectral Reservoir**, a topological AI architecture that replaces Backpropagation with closed-form solutions on spectral expander graphs.

## 🚀 Key Benchmarks
We achieved **hard real-time** performance on commodity hardware by eliminating iterative training in hidden layers:

| Device | Metric | Result | Speedup vs MLP |
| :--- | :--- | :--- | :--- |
| **Legacy CPU (i5-4570, 4th Gen)** | Inference Time | **~287x Faster** | 287x |
| **Mobile (Android ARM64)** | Latency | **< 0.6 ms** | >100x |
| **Throughput** | FPS | **1600+ inf/sec** | N/A |

## 📄 The Paper
Full mathematical derivation, proofs, and the "Poliform Industrial Secret Protocol" details are available on Zenodo:
**[LINK A TU ZENODO AQUÍ]**

## 🔧 How it Works
1.  **Project:** Input data is projected onto a fixed **Ramanujan Graph** ($d$-regular spectral expander).
2.  **Diffuse:** Information propagates via spectral diffusion (mixing time is optimal).
3.  **Solve:** The readout layer is computed analytically (Closed-Form) using Ridge Regression.
4.  **Result:** Deterministic, Green AI that runs on bare metal C++.

## 💻 Code Availability
The C++ kernel for Android and the Python reference implementation are available under the **Polyform Strict License 1.0.0** (Non-commercial research only).

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*Developed by Andrés Sebastián Pirolo (Independent Researcher).*
*Contact: apirolo@abc.gob.ar*