---
license: cc-by-4.0
task_categories:
- other
tags:
- pathfinding
- gpu-computing
- benchmark
- neuromorphic
- navigation
- eikonal-equation
- robotics
- real-time
size_categories:
- n<1K
---

# Optical Neuromorphic Eikonal Solver - Benchmark Datasets

## Overview

Benchmark datasets for evaluating the **Optical Neuromorphic Eikonal Solver**, a GPU-accelerated pathfinding algorithm achieving **30-300× speedup** over CPU Dijkstra.

## 🎯 Key Results

- **134.9× average speedup** vs CPU Dijkstra
- **0.64% mean error** (sub-1% accuracy)
- **1.025× path length** (near-optimal paths)
- **2-4ms per query** on 512×512 grids

## 📊 Dataset Content

5 synthetic pathfinding test cases covering diverse scenarios:

| File | Grid Size | Cells | Obstacles | Speed Field | Difficulty |
|------|-----------|-------|-----------|-------------|------------|
| sparse_128.npz | 128×128 | 16,384 | 10% | Uniform | Easy |
| medium_256.npz | 256×256 | 65,536 | 20% | Uniform | Medium |
| gradient_256.npz | 256×256 | 65,536 | 20% | Gradient | Medium |
| maze_511.npz | 511×511 | 261,121 | 30% (maze) | Uniform | Hard |
| complex_512.npz | 512×512 | 262,144 | 30% | Random | Hard |

Plus: `benchmark_results.csv` with performance metrics

## 📋 Format

Each `.npz` file contains:

```python
{
    'obstacles': np.ndarray,  # (H,W) float32, 1.0=blocked, 0.0=free
    'speeds': np.ndarray,     # (H,W) float32, propagation speed
    'source': np.ndarray,     # (2,) int32, [x,y] start coordinates
    'target': np.ndarray,     # (2,) int32, [x,y] goal coordinates
    'metadata': str           # JSON with provenance info
}
```

## 🔧 Loading Data

```python
import numpy as np
from huggingface_hub import hf_hub_download

# Download dataset
file_path = hf_hub_download(
    repo_id="Agnuxo/optical-neuromorphic-eikonal-benchmarks",
    filename="maze_511.npz",
    repo_type="dataset"
)

# Load
data = np.load(file_path, allow_pickle=True)
obstacles = data['obstacles']
speeds = data['speeds']
source = tuple(data['source'])
target = tuple(data['target'])

print(f"Grid: {obstacles.shape}")
print(f"Start: {source}, Goal: {target}")
```

## 🎮 Interactive Demo

Try the interactive pathfinding demo: [Space Link](https://huggingface.co/spaces/Agnuxo/optical-neuromorphic-pathfinding-demo)

## 📄 Paper & Code

- **Paper**: [GitHub](https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver)
- **Code**: [GitHub Repository](https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver)
- **Author**: [Francisco Angulo de Lafuente](https://huggingface.co/Agnuxo)

## 📚 Citation

```bibtex
@misc{angulo2025optical,
  title={Optical Neuromorphic Eikonal Solver Benchmark Datasets},
  author={Angulo de Lafuente, Francisco},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/Agnuxo/optical-neuromorphic-eikonal-benchmarks}
}
```

## 📜 License

CC BY 4.0 (Creative Commons Attribution 4.0 International)

## 🔗 Links

- Code: https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver
- Kaggle: https://www.kaggle.com/franciscoangulo
- ResearchGate: https://www.researchgate.net/profile/Francisco-Angulo-Lafuente-3