Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

tinyML via YouTube Direct link

Sponsors

9 of 9

9 of 9

Sponsors

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Introduction
  2. 2 Key Problems
  3. 3 Quantization Methods
  4. 4 Key Differences
  5. 5 Straight Through Estimation
  6. 6 Results
  7. 7 Hardware Considerations
  8. 8 QA
  9. 9 Sponsors

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.