Edge AI is no longer a research topic — it's shipping in production IoT products today. TensorFlow Lite makes it practical to run ML inference on devices with as little as 256 MB RAM. Here's our team's step-by-step process.
Step 1: Train and Export Your Model
Start with a standard TensorFlow or Keras model. Once trained, convert it to the TFLite format using the TFLiteConverter:
Step 2: Quantize for Performance
Post-training quantization reduces model size by 4x and latency by 2-3x with minimal accuracy loss. For Raspberry Pi and i.MX8, INT8 quantization is the sweet spot:
Step 3: Deploy on Embedded Linux
Install TFLite runtime on your target device. For Raspberry Pi (ARM64):
Then load and run inference with minimal code:
Benchmark: Raspberry Pi 4 vs i.MX8
- MobileNetV2 image classification: Pi 4 ~50ms, i.MX8 with NPU ~8ms.
- INT8 quantized models run 2-3x faster than FP32 on both platforms.
- For real-time video inference (>15fps), use i.MX8 with hardware NPU.
Common Pitfalls
- Not quantizing — FP32 models are 4x larger and slower on embedded.
- Forgetting thermal throttling — sustained inference heats up Pi 4 significantly.
- Using Python for latency-critical paths — use the C++ TFLite API for production.
FoogleTech's edge AI team has deployed TFLite on Raspberry Pi, NVIDIA Jetson, NXP i.MX8, and STM32 microcontrollers. If you need help with embedded AI integration, contact us for a free consultation.