How to use hardware acceleration for Machine Learning inference on Android
summary
Shows concrete steps to speed on-device ML: use TensorFlow Lite delegates to rewrite model nodes for GPU/DSP/NPU, add the delegate factory when creating an Interpreter, and rely on Play services TF Lite for updates. Reports a real case with 30% performance improvement after an update and estimates ~60% of devices support the GPU delegate (near 99.9% on flagships). Notes hardware acceleration can hurt latency when context switches or model shape make offload inefficient.
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chapters (10)
- 0:00Android ML and hardware acceleration
- 1:51Run ML on Android devices
- 3:58ML Kit vs TensorFlow Lite
- 5:01Hardware acceleration benefits
- 7:27TensorFlow Lite delegates
- 10:18GPU with TensorFlow Lite
- 13:07NN API limitations
- 14:54Acceleration Service for fine-tuning
- 16:16When to use hardware acceleration
- 17:18Q&A
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