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- title: README
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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ title: BrainChip AI
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+ emoji: 🧠
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+ short_description: BrainChip Holdings β€” Neuromorphic Edge AI organization card
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+ <p align="center">
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+ <picture>
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+ <source media="(prefers-color-scheme: dark)" srcset="https://brainchip.com/wp-content/uploads/2023/03/0.-BC-LOGO-on-Black-BG-400x98.png">
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+ <img alt="BrainChip" src="https://brainchipmadev.wpenginepowered.com/wp-content/uploads/2023/03/0.-LOGO_Standard.svg" width="360">
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+ </picture>
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+ </p>
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+ <h1 align="center">Neuromorphic Edge AI</h1>
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+ <p align="center">
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+ Ultra-low-power, real-time AI at the sensor β€” powered by the Akida&trade; event-based processor.
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+ </p>
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+ <p align="center">
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+ <a href="https://brainchip.com"><img alt="Website" src="https://img.shields.io/badge/Website-brainchip.com-6b8cff?style=flat-square"></a>
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+ <a href="https://github.com/Brainchip-Inc"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Brainchip--Inc-24292e?style=flat-square&logo=github"></a>
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+ <a href="https://huggingface.co/BrainChip-AI"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-BrainChip--AI-ffd21e?style=flat-square"></a>
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+ <img alt="Edge AI" src="https://img.shields.io/badge/Focus-Neuromorphic%20Edge%20AI-a78bfa?style=flat-square">
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+ <img alt="On-device" src="https://img.shields.io/badge/On--device-Learning-6b8cff?style=flat-square">
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+ </p>
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+ ---
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+ ## About BrainChip
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+ BrainChip is the worldwide leader in neuromorphic Edge AI on-chip processing and learning. Our Akida&trade; processor delivers ultra-low-power, real-time AI at the sensor β€” without a round-trip to the cloud.
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+ ## What we build
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+ **Akida neuromorphic IP** β€” licensable event-based AI accelerator for SoC integration.
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+ **Akida silicon** β€” standalone neuromorphic processors including the AKD1000 and AKD1500.
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+ **TENNs** β€” Temporal Event-based Neural Networks for audio, vision, biosignals, and time-series.
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+ **On-device learning** β€” on-chip learning without a cloud round-trip.
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+ ## Why event-based at the edge
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+ Event-based processing means Akida only does work when something changes, enabling real-time inference at milliwatts. That unlocks always-on intelligence in hearables, wearables, AR/VR, industrial sensors, medical devices, defense platforms, robotics, and automotive cabins.
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+ ## What's coming to this org
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+ Reference models, example Spaces, and integration recipes for the Akida Gen AI product family. Follow the org to get notified as new material is published.
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+ ## Links
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+ Website β€” [brainchip.com](https://brainchip.com)
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+ GitHub β€” [github.com/Brainchip-Inc](https://github.com/Brainchip-Inc)
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+ ---
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+ <p align="center">&copy; BrainChip Holdings, Inc.</p>