How to Optimize, Validate and Deploy ML Models On Device
summary
Qualcomm AIHub accepts PyTorch or ONNX models, runs a compile job to fuse ops and emit TensorFlow Lite or Qualcomm AI Engine Direct assets, offers an optional quantize job, then profiles and runs inference on real devices (example target: Samsung Galaxy S24) to report per-layer compute placement, latency and memory. The platform hosts over 260 pre-optimized models, claims a real user cut latency by 50x so their app scales to ~85k downloads/day, and provides sample Android apps to drop compiled assets into.
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chapters (23)
- 0:00Qualcomm AIHub intro
- 1:47Deploy ML on device
- 3:52On-device ML challenges
- 4:56AIHub simplifies deployment
- 7:06AIHub key features
- 9:53FullTrack AI success
- 12:19On-device workflow
- 14:04Compile models for runtime
- 16:29Quantize models
- 17:10Profile on real hardware
- 18:12Validate accuracy on device
- 19:37Demo intro
- 20:20Setup AIHub Python API
- 22:07Compile MobileNetV2
- 23:56Visualize optimized graph
- 24:18Profile performance
- 25:42Running inference with sample data
- 27:25Integrate models
- 29:10Android sample app
- 30:13Deploy LLMs on device
- 31:17Local LLM chat demo
- 33:54Resources & community
- 36:22Q&A
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