Episode

Pete Warden — Practical Applications of TinyML

Podcast
Gradient Dissent: Conversations on AI
Published
Oct 21, 2021
Duration seconds
3208
Processing state
failed
Canonical source
https://wandb.ai/site/resources/podcast
Audio
https://podcasts.captivate.fm/media/037cf163-8582-4048-a1c2-e8eac4974f62/gd-pete-warden-v3.mp3
JSON
/v1/public/podcasts/gradient-dissent/episodes/pete-warden-practical-applications-of-tinyml
Markdown
/podcast/gradient-dissent/pete-warden-practical-applications-of-tinyml.md

Actions

  • POST https://stenobird.com/v1/public/podcasts/gradient-dissent/episodes/pete-warden-practical-applications-of-tinyml/transcription-requests
    Idempotently request low-priority transcript generation for this episode.
  • GET https://stenobird.com/podcast/gradient-dissent/pete-warden-practical-applications-of-tinyml.md
    Read the agent-friendly Markdown representation of this episode resource.

Summary

Pete is the Technical Lead of the TensorFlow Micro team, which works on deep learning for mobile and embedded devices. Lukas and Pete talk about hacking a Raspberry Pi to run AlexNet, the power and size constraints of embedded devices, and techniques to reduce model size. Pete also explains real world applications of TensorFlow Lite Micro and shares what it's been like to work on TensorFlow from the beginning. The complete show notes (transcript and links) can be found here: http://wandb.me/gd-pete-warden --- Connect with Pete: 📍 Twitter: https://twitter.com/petewarden 📍 Website: https://petewarden.com/ --- Timestamps: 0:00 Intro 1:23 Hacking a Raspberry Pi to run neural nets 13:50 Model and hardware architectures 18:56 Training a magic wand 21:47 Raspberry Pi vs Arduino 27:51 Reducing model size 33:29 Training on the edge 39:47 What it's like to work on TensorFlow 47:45 Improving datasets and model deployment 53:05 Outro --- Subscribe and listen to our podcast today! 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Google Podcasts: http://wandb.me/google-podcasts​ 👉 Spotify: http://wandb.me/spotify​