Edge Impulse & Android: Democratizing AI at the Edge
summary
Edge Impulse shows an Android workflow: clone the GitHub example repo, drop in the exported C++ library zip, run the provided scripts to fetch TensorFlow Lite binaries and NDK, then call Kotlin runInference with camera frames or accelerometer buffers. A trained model packages as a C++ binary for deploy; training finishes in about 10 minutes, inference runs in ~13 ms (plus ~1 ms overhead), and a tiny demo dataset yields 42% accuracy until more labeled data is collected.
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chapters (14)
- 0:00On-device ML introduction
- 2:14Android inference setup
- 5:05Edge Impulse platform & data
- 9:10Android project with Edge Impulse SDK
- 13:33Demo on Android
- 17:35AI on device vs cloud
- 20:08Edge Impulse activity tracking
- 24:01Data processing & ML blocks
- 27:36Training neural network
- 31:49Deploy to Android & Wear OS
- 35:52Wear OS template & complications
- 37:37Kotlin inference interface
- 41:36Custom C++ inference class
- 44:04Summary & next steps
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