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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
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