AI Deployment on Raspberry Pi with Hardware Acceleration
Raspberry Pi 5 — significant leap for edge AI: 2–3× faster than Pi 4. With hardware accelerators (Hailo-8, Coral USB) becomes serious edge AI platform.
Hardware Acceleration for Pi
Hailo-8 M.2 HAT+: 26 TOPS at 5 W consumption. Specifically designed for Pi 5 (M.2 slot via HAT). YOLOv8n: 30 FPS → 120+ FPS with Hailo-8. Best choice for 2025.
Google Coral USB Accelerator: 4 TOPS, USB 3.0. Works on Pi 4 and Pi 5. Limitation: INT8 TFLite models only.
Intel Neural Compute Stick 2 (Movidius): Deprecated, but exists in legacy projects. USB 3.0.
Stack Without Accelerator (Pure Pi 5)
TFLite + XNNPACK (CPU optimizations ARM Neon): MobileNetV3 classification: ~15 FPS on Pi 5 CPU (vs. 5 FPS on Pi 4). Sufficient for non-urgent tasks.
Llama.cpp on Pi 5: Llama 3.2 1B: 8–12 token/sec. For simple NLP tasks.
Practical Cases
Smart doorbell (face detection with Hailo-8: real-time). Industrial visual inspection (defects): YOLOv8 + Hailo-8, 30 FPS on conveyor. Offline speech assistant: Vosk STT + Llama 3.2 1B (no internet).
OS and Stack
Raspberry Pi OS Bookworm (64-bit). Python 3.11+. TFLite runtime or Hailo SDK. For production — load balancing across multiple Pi as cluster.