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# 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 **[email protected]**. |