Edge AI & Neural Network Solutions

We support various edge AI hardware: ARM Cortex-M with DSP extensions, ARM Cortex-A with NEON, dedicated AI accelerators like Google Edge TPU, NVIDIA Jetson series, Intel Movidius VPUs, specialized MCUs like STM32 with AI capabilities, and FPGA-based custom accelerators. Platform selection is optimized for your performance, power, and cost requirements.

Model optimization involves multiple techniques: quantization to reduce precision (32-bit float to 8-bit integer), pruning to remove redundant weights (40-90% reduction), knowledge distillation to create smaller student models, architecture search for efficient designs, and hardware-aware optimization. We typically achieve 5-10x model size reduction with minimal accuracy loss (under 1%).

Performance varies by model and hardware. For image classification on Cortex-M7, we achieve 5-20 FPS at 50-200mW. On Cortex-A with NEON, 30-60 FPS at 500mW-2W. With dedicated accelerators like Edge TPU, 100-400 FPS at under 2W. For keyword spotting and sensor anomaly detection, inference completes in under 10ms at microamp levels for always-on applications.

Yes, we implement on-device learning for model adaptation and personalization. Techniques include transfer learning with frozen base layers, federated learning for privacy-preserving updates, online learning for continuous adaptation, and periodic cloud-based retraining with edge data. This enables models to adapt to local conditions while maintaining privacy and reducing cloud dependency.

Model security includes encrypted model storage, secure enclave execution (TrustZone, TEE), input validation to detect adversarial examples, model watermarking to prevent theft, and runtime integrity verification. For sensitive applications, we implement differential privacy, homomorphic encryption for inference, and adversarial training to improve robustness against attacks.