A lightweight AI framework designed for microcontrollers with privacy-preserving computation. Perfect for edge devices intelligence without compromising security.
Engineered for minimal resource consumption while delivering robust AI inference capabilities on edge devices.
Optimized to run efficiently on microcontrollers with as little as 128KB of memory. Uses modular components to reduce overhead.
Implements federated learning and encrypted inference to ensure user data remains secure on device.
Neural Circuit processes data locally, eliminating the need for cloud connectivity while maintaining full control over sensitive information.
Neural Circuit's low resource requirements make it ideal for these critical use cases
Enables real-time health monitoring without cloud dependency in wearable devices and implantable systems.
Powers intelligent smart home systems with voice and gesture recognition without exposing user data.
Implements predictive maintenance systems with edge-based anomaly detection for critical manufacturing equipment.
Begin building with Neural Circuit using our simple integration and deployment process.
Start by forking our GitHub repository and setting up the development environment.
git clone https://github.com/eblc/neural-circuit
Use our model compiler to convert your TensorFlow/PyTorch models into optimized EdgeQ format.
cargo run --bin edgeq-compiler -i model.onnx -o optimized.bin
Flash the compiled model to your microcontroller and begin real-time edge inference.
edgeq flash --device /dev/ttyUSB0 --file optimized.bin