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---
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license: cc-by-4.0
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task_categories:
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- other
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tags:
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- pathfinding
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- gpu-computing
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- benchmark
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- neuromorphic
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- navigation
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- eikonal-equation
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- robotics
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- real-time
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size_categories:
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- n<1K
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---
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# Optical Neuromorphic Eikonal Solver - Benchmark Datasets
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## Overview
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Benchmark datasets for evaluating the **Optical Neuromorphic Eikonal Solver**, a GPU-accelerated pathfinding algorithm achieving **30-300× speedup** over CPU Dijkstra.
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## 🎯 Key Results
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- **134.9× average speedup** vs CPU Dijkstra
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- **0.64% mean error** (sub-1% accuracy)
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- **1.025× path length** (near-optimal paths)
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- **2-4ms per query** on 512×512 grids
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## 📊 Dataset Content
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5 synthetic pathfinding test cases covering diverse scenarios:
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| File | Grid Size | Cells | Obstacles | Speed Field | Difficulty |
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|------|-----------|-------|-----------|-------------|------------|
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| sparse_128.npz | 128×128 | 16,384 | 10% | Uniform | Easy |
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| medium_256.npz | 256×256 | 65,536 | 20% | Uniform | Medium |
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| gradient_256.npz | 256×256 | 65,536 | 20% | Gradient | Medium |
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| maze_511.npz | 511×511 | 261,121 | 30% (maze) | Uniform | Hard |
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| complex_512.npz | 512×512 | 262,144 | 30% | Random | Hard |
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Plus: `benchmark_results.csv` with performance metrics
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## 📋 Format
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Each `.npz` file contains:
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```python
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{
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'obstacles': np.ndarray, # (H,W) float32, 1.0=blocked, 0.0=free
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'speeds': np.ndarray, # (H,W) float32, propagation speed
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'source': np.ndarray, # (2,) int32, [x,y] start coordinates
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'target': np.ndarray, # (2,) int32, [x,y] goal coordinates
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'metadata': str # JSON with provenance info
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}
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```
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## 🔧 Loading Data
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```python
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import numpy as np
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from huggingface_hub import hf_hub_download
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# Download dataset
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file_path = hf_hub_download(
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repo_id="Agnuxo/optical-neuromorphic-eikonal-benchmarks",
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filename="maze_511.npz",
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repo_type="dataset"
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)
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# Load
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data = np.load(file_path, allow_pickle=True)
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obstacles = data['obstacles']
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speeds = data['speeds']
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source = tuple(data['source'])
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target = tuple(data['target'])
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print(f"Grid: {obstacles.shape}")
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print(f"Start: {source}, Goal: {target}")
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```
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## 🎮 Interactive Demo
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Try the interactive pathfinding demo: [Space Link](https://huggingface.co/spaces/Agnuxo/optical-neuromorphic-pathfinding-demo)
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## 📄 Paper & Code
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- **Paper**: [GitHub](https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver)
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- **Code**: [GitHub Repository](https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver)
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- **Author**: [Francisco Angulo de Lafuente](https://huggingface.co/Agnuxo)
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## 📚 Citation
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```bibtex
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@misc{angulo2025optical,
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title={Optical Neuromorphic Eikonal Solver Benchmark Datasets},
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author={Angulo de Lafuente, Francisco},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/datasets/Agnuxo/optical-neuromorphic-eikonal-benchmarks}
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}
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```
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## 📜 License
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CC BY 4.0 (Creative Commons Attribution 4.0 International)
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## 🔗 Links
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- Code: https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver
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- Kaggle: https://www.kaggle.com/franciscoangulo
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- ResearchGate: https://www.researchgate.net/profile/Francisco-Angulo-Lafuente-3
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