[Workshop] tinyML Talks: Low-Power Computer Vision

Published: by Creative Commons Licence (Last updated: )

tinyML Talks - Yung-Hsiang Lu: Low-Power Computer Vision1

Hierarchical Neural Networks

By utilizing hierarchical neural network, we can separate the big neural network into much small ones, hence reduce the training time and inference power consumption. However, it might increase the latency.

Hierarchical Neural Networks

The Existing Technologies for Making Computer Vision Energy-Efficient

This presentation also surveyed the existing technologies for making computer vision energy-efficient, including

  1. parameter quantization and pruning;
  2. compressed convolutional filters and matrix factorization;
  3. network architecture search;
  4. knowledge distillation.
  1. tinyML, YouTube