Edge AI

TinyML, on-device inference, model compression, and AI at the edge.

Running ML models on microcontrollers, low-power devices, and constrained hardware.

On-Device Inference

Mobile deployment, hardware accelerators, and real-time edge inference.

Model Compression

Pruning, quantization, distillation, and shrinking models for edge deployment.

Federated Learning

Privacy-preserving distributed training across decentralized devices.

Hardware Accelerators

NPUs, TPUs, FPGAs, and specialized chips for edge AI inference.

Edge Deployment Frameworks

TensorFlow Lite, ONNX Runtime, CoreML, and cross-platform edge SDKs.

Privacy-Preserving AI

Differential privacy, secure computation, and confidential ML inference.