# EmbedNeural

*On-device multimodal embedding model enabling instant, private NPU-powered visual search.*

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## Quickstart

[Instruction](https://sdk.nexa.ai/model/EmbedNeural)

## Model Description

**EmbedNeural** is the world’s first multimodal embedding model purpose-built for **Qualcomm Hexagon NPU** devices. It enables **instant, private, battery-efficient** natural-language image search directly on laptops, phones, XR, and edge devices — with no cloud and no uploads.

The model continuously indexes local images using NPU acceleration, turning unorganized photo folders into a fully searchable visual database that runs entirely on-device.

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## Key Features

### ⚡ NPU-accelerated multimodal embeddings  
Optimized for Qualcomm NPUs to deliver sub-second search and dramatically lower power consumption.

### 🔍 Natural-language visual search  
Query thousands of images instantly using everyday language (e.g., “green bedroom aesthetic”, “cat wearing sunglasses”).

### 🔒 100% local and private  
All computation stays on-device. No cloud. No upload. No tracking. 

### 🔋 Ultra-low power  
Continuous background indexing uses ~10× less power than CPU/GPU methods, enabling true always-on search.

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## Why It Matters

People save thousands of images — memes, screenshots, design inspo, photos — but struggle to find them when needed. Cloud solutions compromise privacy; CPU/GPU search drains battery. 

EmbedNeural removes these tradeoffs by combining:  
- **Instant retrieval** (~0.03s across thousands of images)  
- **Continuous local indexing**  
- **Zero data upload**  
- **NPU-optimized efficiency for daily use**  

This makes visual search something you can actually use **every day**, not just when plugged in. 

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## Use Cases

- **Personal image libraries:** Rediscover memes, screenshots, and old photos instantly.  
- **Creative workflows:** Search moodboards and visual references with natural language.  
- **Edge & embedded systems:** Efficient multimodal search for mobile, XR, IoT, and automotive.

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## Performance Highlights

- Sub-second search even across large image libraries
- ~10× lower power consumption vs CPU/GPU search
- Stable always-on indexing without thermal or battery issues

## License  
This model is released under the **Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)** license.  
Non-commercial use, modification, and redistribution are permitted with attribution.  
For commercial licensing, please contact **dev@nexa.ai**.