How to Optimize, Validate and Deploy ML Models on Device
summary
Platform compiles, quantizes and profiles models into deployable artifacts for Android using TF Lite, ONNX or Qualcomm AI Engine (QNN) runtimes, running each profile on a real device farm run 100 times to report latency, memory and compute residency. Over 800 companies and ~5,000 users have submitted ~99,000 jobs; the web repo now lists ~125 pre-optimized models, a Python client submits compilation in five lines, and sample Android apps show NPU vs CPU performance and PSNR outputs.
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chapters (15)
- 0:00On-device AI optimization intro
- 1:09Deploy ML on device
- 3:36Device compute units
- 5:00Qualcomm AIHub overview
- 7:46AIHub platform features
- 11:38Submit compilation jobs
- 16:59Profile models for performance
- 19:03Validate model accuracy
- 20:48Demo AIHub workflow
- 26:24Deploy models in Android
- 33:06Run LLMs on Android
- 36:14Developer resources
- 47:11Handle multiple chipsets
- 51:24Optimize for Qualcomm variants
- 56:45Q&A
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