Code for Chapter 15: Becoming a Maker: Exploring Embedded AI at the Edge

Guest author Sam Sterckval brings deep learning to low-power devices as he showcases a range of AI-capable edge devices with varying processing power and cost including Raspberry Pi, NVIDIA Jetson Nano, Google Coral, Intel Movidius, PYNQ-Z2 FPGA, opening the doors for robotics and maker projects.

Code

Go through the code in the following order:

1. benchmark.py: This script will make a prediction on the cat.jpg file 250 times and measure how long it takes to run.
2. benchmark_tflite.py: This script will make a prediction on the cat.jpg file 250 times using a TensorFlow Lite model and measure how long it takes to run.
3. benchmark_edgetpu.py: This script can make a prediction on the cat.jpg file 250 times on a Raspberry Pi and measure how long it takes to run.
4. benchmark_jetson.py: This script can make a prediction on the cat.jpg file 250 times on a Jetson Nano and measure how long it takes to run.

Please note that all the examples on edge devices showcased above work only on TensorFlow 1.x as of now.